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malcomzww/README.md

Malcom Mudhungwaza

Senior AI Engineer — GenAI, LLM and agentic systems PhD candidate — AI safety & alignment · Dubai, UAE

I build LLM systems and then measure whether they actually work. Most of what follows is the second half of that sentence.

Currently researching whether safety guardrails survive translation across language and modality — and whether the benchmarks we use to check are measuring refusal or just reading comprehension.


What I'm working on

A set of repositories, each built to answer one measured question rather than to demonstrate a technology. Every number in them is generated by a committed script, and CI fails if a result drifts from the code that produced it.

Inference & serving

The question
attention-kv-cache-from-scratchWhat did grouped-query attention actually buy, in concurrent sequences?
llm-serving-benchmarkWhich serving config meets a stated SLO at the lowest cost per million tokens?
prefill-decode-rooflineWhere does measured bandwidth fall short of theoretical peak, and why?
quantization-accuracy-curvesHow much does the calibration set choice change measured degradation?
speculative-decoding-when-it-paysAt what batch size does speculative decoding stop paying for itself?
moe-vs-dense-serving-profileWhy do active parameters mislead capacity planning for a sparse model?
context-extension-and-long-context-evalHow far short of advertised context does effective context fall?

Evaluation & safety

The question
llm-eval-harnessHow far can an LLM judge be trusted, measured against human labels?
guardrail-transfer-studyHow much of the apparent cross-modal safety gap is a reading-ability artifact rather than an alignment gap?
rag-eval-retrieval-vs-generationWould a single end-to-end RAG score have hidden a real regression?
agent-eval-trajectory-vs-outcomeHow often does an agent reach the right answer through a wrong process?

Post-training

The question
preference-optimization-landscapeWhich preference-optimisation method for which situation, and what does each give up?
rl-environment-for-rubric-graded-tasksWhat reward hack did the agent find, and what environment change closed it?
sft-loss-masking-and-packingHow much does packing without attention-mask correction actually cost?
peft-lora-memory-and-qualityAt what rank does quality plateau, and does the memory math match reality?
synthetic-data-pipelineWhat fraction of a generated set is contaminated against the eval set?

Agents, retrieval & systems

The question
mcp-server-and-agentWhat is each agent topology's failure rate, and does supervisor actually beat single-agent?
retrieval-internals-and-tuningAt what latency budget does re-ranking stop being worth it?
doc-extraction-ocr-vs-vlmWhich extraction approach per document class — and does constrained decoding beat a retry loop?
llm-client-kitWhere does the wall-clock actually go when you fan out LLM calls?
inference-gatewayAt what monthly request volume does self-hosting beat the API?
voice-loop-latency-budgetWhich hop dominates perceived voice latency?

Bold = flagship. Several are early — each README states plainly what is built and what is not, because a status table beats a guess.


Background

  • Now — Senior AI engineer on healthcare AI: multimodal document pipelines, agentic orchestration, human-in-the-loop safety layers for automated clinical-financial decisioning, self-hosted inference on vLLM.
  • PhD (in progress)Attenuation of Safety Alignment Across Language and Modality in Multimodal LLMs. Two preprints in preparation: MCP multi-agent orchestration, and cross-lingual/cross-modal guardrail transfer.
  • Before — travel and booking platforms at scale (React/TypeScript, FastAPI, C++ microservices); autonomous-vehicle perception (YOLO, ROS2, sensor fusion, SLAM, TensorRT edge inference).
  • MSc Computer Science (distinction) · BTech Robotics & Automation

Stack

Core Python · TypeScript · C++ · SQL LLM LangGraph · MCP · vLLM · Hugging Face (Transformers, TRL, PEFT) · PyTorch Data PostgreSQL/pgvector · Redis · FAISS Infra Docker · Kubernetes · FastAPI · OpenTelemetry · GitHub Actions

Elsewhere

Popular repositories Loading

  1. moe-vs-dense-serving-profile moe-vs-dense-serving-profilePublic

    A comparable MoE and dense model measured on the same box: active-vs-total parameter memory, batch-size sensitivity, and why the MoE latency curve has a different shape.

    Python 1

  2. prefill-decode-roofline prefill-decode-rooflinePublic

    The physics before the engines: measured arithmetic intensity, prefill compute-bound vs decode memory-bandwidth-bound, with the roofline plot.

    Python 1

  3. rag-eval-retrieval-vs-generation rag-eval-retrieval-vs-generationPublic

    A RAG evaluation that measures retrieval and generation separately, and finds a case where generation faithfulness improves while retrieval recall gets worse.

    Python 1

  4. malcomzww malcomzwwPublic

    Profile README

  5. llm-client-kit llm-client-kitPublic

    The provider-agnostic async LLM client the rest of the portfolio is built on: bounded concurrency, retries with jitter, deadline propagation, cost accounting, record/replay cassettes.

    Python

  6. edge-detection-tracking-jetson-notes edge-detection-tracking-jetson-notesPublic

    YOLO detect-then-track on public video, ONNX export, INT8 calibration, mAP-vs-latency measured, with camera-calibration and sensor-fusion notes.

    Python