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mandipat/README.md

Hey, I'm Rithik 👋

I build things with LLMs and then break them until they work properly.

Right now I'm an AI Engineer at IFF, where I somehow ended up wearing every hat — architect, platform engineer, the "why is this costing us $40k/month" guy, and occasionally the person who deploys things at 2am.

Lately I've also been putting on the product hat for our enterprise LLM Gateway — talking to teams, figuring out what's actually painful, and shaping the thing into something people want to use, not just something that exists.


What I'm building right now

Enterprise LLM Gateway — the boring-but-critical infrastructure that sits between your team and the LLM providers. Governance, cost visibility, smart routing. The stuff nobody wants to build until the bill arrives. I'm owning this end-to-end: the architecture, the product decisions, the roadmap.

Autonomous agent orchestration — multi-agent systems that actually finish their tasks without a human babysitting them. MCP-style tool-calling, sandboxed remote execution, the works.

A platform where scientists just... run their ML jobs — talking to end users about their problems, finding better solutions for the people working on it, and facilitating a place for ML deployment. SageMaker, Kubernetes, Terraform, autoscaling. They click a button, models train. I lose sleep over the cost.


How my brain works

I don't have a "stack" — I have a problem, and then I find whatever solves it.

  • Monday: building an LLM router that picks the cheapest model that's still good enough
  • Tuesday: debugging why an agent decided to query Snowflake 47 times in a loop
  • Wednesday: writing Terraform for the 900th time
  • Thursday: talking to scientists about why their pipeline is slow and sketching a better architecture on a whiteboard
  • Friday: asking myself why I didn't just become a surfing instructor

Things I reach for

python · fastapi · kubernetes · terraform · aws (too much of it) · openai/anthropic apis · langchain · react · docker · go (learning) · postgres · grafana


When I'm not coding

Surfing · skiing · hiking · biking · reading papers about things I'll never implement · explaining to my family what I do for a living (still failing at this one)


Say hi

If you're building something interesting with LLMs, agents, or ML infrastructure — or if you just want to argue about whether RAG is dead — I'm always down to chat.

mandipat@usc.edu · linkedin(something's never change, it's not up to date)


Currently obsessed with: making agents reliable enough that I trust them to run overnight without waking up to a $10k bill

Pinned Loading

  1. ML-Model-Deployment-HealthcareML-Model-Deployment-HealthcarePublic

    Real world heathcare predictive analytical projects that were deployed on Google cloud platform

    Jupyter Notebook

  2. AWS-SagemakerAWS-SagemakerPublic

    Python

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
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mandipat/README.md

Hey, I'm Rithik 👋

I build things with LLMs and then break them until they work properly.

Right now I'm an AI Engineer at IFF, where I somehow ended up wearing every hat — architect, platform engineer, the "why is this costing us $40k/month" guy, and occasionally the person who deploys things at 2am.

Lately I've also been putting on the product hat for our enterprise LLM Gateway — talking to teams, figuring out what's actually painful, and shaping the thing into something people want to use, not just something that exists.


What I'm building right now

Enterprise LLM Gateway — the boring-but-critical infrastructure that sits between your team and the LLM providers. Governance, cost visibility, smart routing. The stuff nobody wants to build until the bill arrives. I'm owning this end-to-end: the architecture, the product decisions, the roadmap.

Autonomous agent orchestration — multi-agent systems that actually finish their tasks without a human babysitting them. MCP-style tool-calling, sandboxed remote execution, the works.

A platform where scientists just... run their ML jobs — talking to end users about their problems, finding better solutions for the people working on it, and facilitating a place for ML deployment. SageMaker, Kubernetes, Terraform, autoscaling. They click a button, models train. I lose sleep over the cost.


How my brain works

I don't have a "stack" — I have a problem, and then I find whatever solves it.

  • Monday: building an LLM router that picks the cheapest model that's still good enough
  • Tuesday: debugging why an agent decided to query Snowflake 47 times in a loop
  • Wednesday: writing Terraform for the 900th time
  • Thursday: talking to scientists about why their pipeline is slow and sketching a better architecture on a whiteboard
  • Friday: asking myself why I didn't just become a surfing instructor

Things I reach for

python · fastapi · kubernetes · terraform · aws (too much of it) · openai/anthropic apis · langchain · react · docker · go (learning) · postgres · grafana


When I'm not coding

Surfing · skiing · hiking · biking · reading papers about things I'll never implement · explaining to my family what I do for a living (still failing at this one)


Say hi

If you're building something interesting with LLMs, agents, or ML infrastructure — or if you just want to argue about whether RAG is dead — I'm always down to chat.

mandipat@usc.edu · linkedin(something's never change, it's not up to date)


Currently obsessed with: making agents reliable enough that I trust them to run overnight without waking up to a $10k bill

Pinned Loading

  1. ML-Model-Deployment-HealthcareML-Model-Deployment-HealthcarePublic

    Real world heathcare predictive analytical projects that were deployed on Google cloud platform

    Jupyter Notebook

  2. AWS-SagemakerAWS-SagemakerPublic

    Python

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Report abuse
mandipat/README.md

Hey, I'm Rithik 👋

I build things with LLMs and then break them until they work properly.

Right now I'm an AI Engineer at IFF, where I somehow ended up wearing every hat — architect, platform engineer, the "why is this costing us $40k/month" guy, and occasionally the person who deploys things at 2am.

Lately I've also been putting on the product hat for our enterprise LLM Gateway — talking to teams, figuring out what's actually painful, and shaping the thing into something people want to use, not just something that exists.


What I'm building right now

Enterprise LLM Gateway — the boring-but-critical infrastructure that sits between your team and the LLM providers. Governance, cost visibility, smart routing. The stuff nobody wants to build until the bill arrives. I'm owning this end-to-end: the architecture, the product decisions, the roadmap.

Autonomous agent orchestration — multi-agent systems that actually finish their tasks without a human babysitting them. MCP-style tool-calling, sandboxed remote execution, the works.

A platform where scientists just... run their ML jobs — talking to end users about their problems, finding better solutions for the people working on it, and facilitating a place for ML deployment. SageMaker, Kubernetes, Terraform, autoscaling. They click a button, models train. I lose sleep over the cost.


How my brain works

I don't have a "stack" — I have a problem, and then I find whatever solves it.

  • Monday: building an LLM router that picks the cheapest model that's still good enough
  • Tuesday: debugging why an agent decided to query Snowflake 47 times in a loop
  • Wednesday: writing Terraform for the 900th time
  • Thursday: talking to scientists about why their pipeline is slow and sketching a better architecture on a whiteboard
  • Friday: asking myself why I didn't just become a surfing instructor

Things I reach for

python · fastapi · kubernetes · terraform · aws (too much of it) · openai/anthropic apis · langchain · react · docker · go (learning) · postgres · grafana


When I'm not coding

Surfing · skiing · hiking · biking · reading papers about things I'll never implement · explaining to my family what I do for a living (still failing at this one)


Say hi

If you're building something interesting with LLMs, agents, or ML infrastructure — or if you just want to argue about whether RAG is dead — I'm always down to chat.

mandipat@usc.edu · linkedin(something's never change, it's not up to date)


Currently obsessed with: making agents reliable enough that I trust them to run overnight without waking up to a $10k bill

Pinned Loading

  1. ML-Model-Deployment-HealthcareML-Model-Deployment-HealthcarePublic

    Real world heathcare predictive analytical projects that were deployed on Google cloud platform

    Jupyter Notebook

  2. AWS-SagemakerAWS-SagemakerPublic

    Python

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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mandipat/README.md

Hey, I'm Rithik 👋

I build things with LLMs and then break them until they work properly.

Right now I'm an AI Engineer at IFF, where I somehow ended up wearing every hat — architect, platform engineer, the "why is this costing us $40k/month" guy, and occasionally the person who deploys things at 2am.

Lately I've also been putting on the product hat for our enterprise LLM Gateway — talking to teams, figuring out what's actually painful, and shaping the thing into something people want to use, not just something that exists.


What I'm building right now

Enterprise LLM Gateway — the boring-but-critical infrastructure that sits between your team and the LLM providers. Governance, cost visibility, smart routing. The stuff nobody wants to build until the bill arrives. I'm owning this end-to-end: the architecture, the product decisions, the roadmap.

Autonomous agent orchestration — multi-agent systems that actually finish their tasks without a human babysitting them. MCP-style tool-calling, sandboxed remote execution, the works.

A platform where scientists just... run their ML jobs — talking to end users about their problems, finding better solutions for the people working on it, and facilitating a place for ML deployment. SageMaker, Kubernetes, Terraform, autoscaling. They click a button, models train. I lose sleep over the cost.


How my brain works

I don't have a "stack" — I have a problem, and then I find whatever solves it.

  • Monday: building an LLM router that picks the cheapest model that's still good enough
  • Tuesday: debugging why an agent decided to query Snowflake 47 times in a loop
  • Wednesday: writing Terraform for the 900th time
  • Thursday: talking to scientists about why their pipeline is slow and sketching a better architecture on a whiteboard
  • Friday: asking myself why I didn't just become a surfing instructor

Things I reach for

python · fastapi · kubernetes · terraform · aws (too much of it) · openai/anthropic apis · langchain · react · docker · go (learning) · postgres · grafana


When I'm not coding

Surfing · skiing · hiking · biking · reading papers about things I'll never implement · explaining to my family what I do for a living (still failing at this one)


Say hi

If you're building something interesting with LLMs, agents, or ML infrastructure — or if you just want to argue about whether RAG is dead — I'm always down to chat.

mandipat@usc.edu · linkedin(something's never change, it's not up to date)


Currently obsessed with: making agents reliable enough that I trust them to run overnight without waking up to a $10k bill

Pinned Loading

  1. ML-Model-Deployment-HealthcareML-Model-Deployment-HealthcarePublic

    Real world heathcare predictive analytical projects that were deployed on Google cloud platform

    Jupyter Notebook

  2. AWS-SagemakerAWS-SagemakerPublic

    Python

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
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mandipat/README.md

Hey, I'm Rithik 👋

I build things with LLMs and then break them until they work properly.

Right now I'm an AI Engineer at IFF, where I somehow ended up wearing every hat — architect, platform engineer, the "why is this costing us $40k/month" guy, and occasionally the person who deploys things at 2am.

Lately I've also been putting on the product hat for our enterprise LLM Gateway — talking to teams, figuring out what's actually painful, and shaping the thing into something people want to use, not just something that exists.


What I'm building right now

Enterprise LLM Gateway — the boring-but-critical infrastructure that sits between your team and the LLM providers. Governance, cost visibility, smart routing. The stuff nobody wants to build until the bill arrives. I'm owning this end-to-end: the architecture, the product decisions, the roadmap.

Autonomous agent orchestration — multi-agent systems that actually finish their tasks without a human babysitting them. MCP-style tool-calling, sandboxed remote execution, the works.

A platform where scientists just... run their ML jobs — talking to end users about their problems, finding better solutions for the people working on it, and facilitating a place for ML deployment. SageMaker, Kubernetes, Terraform, autoscaling. They click a button, models train. I lose sleep over the cost.


How my brain works

I don't have a "stack" — I have a problem, and then I find whatever solves it.

  • Monday: building an LLM router that picks the cheapest model that's still good enough
  • Tuesday: debugging why an agent decided to query Snowflake 47 times in a loop
  • Wednesday: writing Terraform for the 900th time
  • Thursday: talking to scientists about why their pipeline is slow and sketching a better architecture on a whiteboard
  • Friday: asking myself why I didn't just become a surfing instructor

Things I reach for

python · fastapi · kubernetes · terraform · aws (too much of it) · openai/anthropic apis · langchain · react · docker · go (learning) · postgres · grafana


When I'm not coding

Surfing · skiing · hiking · biking · reading papers about things I'll never implement · explaining to my family what I do for a living (still failing at this one)


Say hi

If you're building something interesting with LLMs, agents, or ML infrastructure — or if you just want to argue about whether RAG is dead — I'm always down to chat.

mandipat@usc.edu · linkedin(something's never change, it's not up to date)


Currently obsessed with: making agents reliable enough that I trust them to run overnight without waking up to a $10k bill

Pinned Loading

  1. ML-Model-Deployment-HealthcareML-Model-Deployment-HealthcarePublic

    Real world heathcare predictive analytical projects that were deployed on Google cloud platform

    Jupyter Notebook

  2. AWS-SagemakerAWS-SagemakerPublic

    Python

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
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Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

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Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

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Report abuse
mandipat/README.md

Hey, I'm Rithik 👋

I build things with LLMs and then break them until they work properly.

Right now I'm an AI Engineer at IFF, where I somehow ended up wearing every hat — architect, platform engineer, the "why is this costing us $40k/month" guy, and occasionally the person who deploys things at 2am.

Lately I've also been putting on the product hat for our enterprise LLM Gateway — talking to teams, figuring out what's actually painful, and shaping the thing into something people want to use, not just something that exists.


What I'm building right now

Enterprise LLM Gateway — the boring-but-critical infrastructure that sits between your team and the LLM providers. Governance, cost visibility, smart routing. The stuff nobody wants to build until the bill arrives. I'm owning this end-to-end: the architecture, the product decisions, the roadmap.

Autonomous agent orchestration — multi-agent systems that actually finish their tasks without a human babysitting them. MCP-style tool-calling, sandboxed remote execution, the works.

A platform where scientists just... run their ML jobs — talking to end users about their problems, finding better solutions for the people working on it, and facilitating a place for ML deployment. SageMaker, Kubernetes, Terraform, autoscaling. They click a button, models train. I lose sleep over the cost.


How my brain works

I don't have a "stack" — I have a problem, and then I find whatever solves it.

  • Monday: building an LLM router that picks the cheapest model that's still good enough
  • Tuesday: debugging why an agent decided to query Snowflake 47 times in a loop
  • Wednesday: writing Terraform for the 900th time
  • Thursday: talking to scientists about why their pipeline is slow and sketching a better architecture on a whiteboard
  • Friday: asking myself why I didn't just become a surfing instructor

Things I reach for

python · fastapi · kubernetes · terraform · aws (too much of it) · openai/anthropic apis · langchain · react · docker · go (learning) · postgres · grafana


When I'm not coding

Surfing · skiing · hiking · biking · reading papers about things I'll never implement · explaining to my family what I do for a living (still failing at this one)


Say hi

If you're building something interesting with LLMs, agents, or ML infrastructure — or if you just want to argue about whether RAG is dead — I'm always down to chat.

mandipat@usc.edu · linkedin(something's never change, it's not up to date)


Currently obsessed with: making agents reliable enough that I trust them to run overnight without waking up to a $10k bill

Pinned Loading

  1. ML-Model-Deployment-HealthcareML-Model-Deployment-HealthcarePublic

    Real world heathcare predictive analytical projects that were deployed on Google cloud platform

    Jupyter Notebook

  2. AWS-SagemakerAWS-SagemakerPublic

    Python

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
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Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

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Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

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Report abuse
mandipat/README.md

Hey, I'm Rithik 👋

I build things with LLMs and then break them until they work properly.

Right now I'm an AI Engineer at IFF, where I somehow ended up wearing every hat — architect, platform engineer, the "why is this costing us $40k/month" guy, and occasionally the person who deploys things at 2am.

Lately I've also been putting on the product hat for our enterprise LLM Gateway — talking to teams, figuring out what's actually painful, and shaping the thing into something people want to use, not just something that exists.


What I'm building right now

Enterprise LLM Gateway — the boring-but-critical infrastructure that sits between your team and the LLM providers. Governance, cost visibility, smart routing. The stuff nobody wants to build until the bill arrives. I'm owning this end-to-end: the architecture, the product decisions, the roadmap.

Autonomous agent orchestration — multi-agent systems that actually finish their tasks without a human babysitting them. MCP-style tool-calling, sandboxed remote execution, the works.

A platform where scientists just... run their ML jobs — talking to end users about their problems, finding better solutions for the people working on it, and facilitating a place for ML deployment. SageMaker, Kubernetes, Terraform, autoscaling. They click a button, models train. I lose sleep over the cost.


How my brain works

I don't have a "stack" — I have a problem, and then I find whatever solves it.

  • Monday: building an LLM router that picks the cheapest model that's still good enough
  • Tuesday: debugging why an agent decided to query Snowflake 47 times in a loop
  • Wednesday: writing Terraform for the 900th time
  • Thursday: talking to scientists about why their pipeline is slow and sketching a better architecture on a whiteboard
  • Friday: asking myself why I didn't just become a surfing instructor

Things I reach for

python · fastapi · kubernetes · terraform · aws (too much of it) · openai/anthropic apis · langchain · react · docker · go (learning) · postgres · grafana


When I'm not coding

Surfing · skiing · hiking · biking · reading papers about things I'll never implement · explaining to my family what I do for a living (still failing at this one)


Say hi

If you're building something interesting with LLMs, agents, or ML infrastructure — or if you just want to argue about whether RAG is dead — I'm always down to chat.

mandipat@usc.edu · linkedin(something's never change, it's not up to date)


Currently obsessed with: making agents reliable enough that I trust them to run overnight without waking up to a $10k bill

Pinned Loading

  1. ML-Model-Deployment-HealthcareML-Model-Deployment-HealthcarePublic

    Real world heathcare predictive analytical projects that were deployed on Google cloud platform

    Jupyter Notebook

  2. AWS-SagemakerAWS-SagemakerPublic

    Python

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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mandipat/README.md

Hey, I'm Rithik 👋

I build things with LLMs and then break them until they work properly.

Right now I'm an AI Engineer at IFF, where I somehow ended up wearing every hat — architect, platform engineer, the "why is this costing us $40k/month" guy, and occasionally the person who deploys things at 2am.

Lately I've also been putting on the product hat for our enterprise LLM Gateway — talking to teams, figuring out what's actually painful, and shaping the thing into something people want to use, not just something that exists.


What I'm building right now

Enterprise LLM Gateway — the boring-but-critical infrastructure that sits between your team and the LLM providers. Governance, cost visibility, smart routing. The stuff nobody wants to build until the bill arrives. I'm owning this end-to-end: the architecture, the product decisions, the roadmap.

Autonomous agent orchestration — multi-agent systems that actually finish their tasks without a human babysitting them. MCP-style tool-calling, sandboxed remote execution, the works.

A platform where scientists just... run their ML jobs — talking to end users about their problems, finding better solutions for the people working on it, and facilitating a place for ML deployment. SageMaker, Kubernetes, Terraform, autoscaling. They click a button, models train. I lose sleep over the cost.


How my brain works

I don't have a "stack" — I have a problem, and then I find whatever solves it.

  • Monday: building an LLM router that picks the cheapest model that's still good enough
  • Tuesday: debugging why an agent decided to query Snowflake 47 times in a loop
  • Wednesday: writing Terraform for the 900th time
  • Thursday: talking to scientists about why their pipeline is slow and sketching a better architecture on a whiteboard
  • Friday: asking myself why I didn't just become a surfing instructor

Things I reach for

python · fastapi · kubernetes · terraform · aws (too much of it) · openai/anthropic apis · langchain · react · docker · go (learning) · postgres · grafana


When I'm not coding

Surfing · skiing · hiking · biking · reading papers about things I'll never implement · explaining to my family what I do for a living (still failing at this one)


Say hi

If you're building something interesting with LLMs, agents, or ML infrastructure — or if you just want to argue about whether RAG is dead — I'm always down to chat.

mandipat@usc.edu · linkedin(something's never change, it's not up to date)


Currently obsessed with: making agents reliable enough that I trust them to run overnight without waking up to a $10k bill

Pinned Loading

  1. ML-Model-Deployment-HealthcareML-Model-Deployment-HealthcarePublic

    Real world heathcare predictive analytical projects that were deployed on Google cloud platform

    Jupyter Notebook

  2. AWS-SagemakerAWS-SagemakerPublic

    Python