00 — indexv2026.04

I build the unglamorous parts of machine learning systems.

Forward Deployed AI Engineer at Kodamai. Production agents, LLM pipelines, and the plumbing between models and products. I write Python, Rust, Triton kernels when nothing else will do — and I patch the ML libraries I depend on.
yrs in ML
4+
merged PRs
30+
flax rank
#5
domains
6
01 — about~ 184 words

I build production AI systems — agents, LLM pipelines, and automated workflows that replace manual processes in real business operations. My background spans healthcare, supply chain, enterprise automation, and open-source ML infrastructure.

Currently a Forward Deployed AI Engineer at Kodamai, where I act as the primary technical partner for enterprise clients across multiple simultaneous engagements. Previously built a physician-facing clinical AI platform at Exora AI that passed HIPAA and Singapore PDPA audit and was approved for live patient use.

My foundation is in classical and deep ML: XGBoost pipelines at scale, causal inference, computer vision (YOLOv11, segmentation, SAM 2), and fine-tuning foundation models with LoRA and modern quantization (AWQ, GPTQ, GGUF). I work daily with the current generation of vision-language models — Qwen3-VL, GLM-4.6V, InternVL3.5, MiniCPM-V — on document intelligence, medical imaging, ANPR, and long-context multimodal reasoning that now stretches to 256K interleaved tokens.

I contribute to open-source ML projects in my own time. Ranked #5 on the official Google/Flax contributor leaderboard over the last 12 months, with patches merged into the Flax NNX core. Six merged PRs in CPython and three in Uber/CausalML.

02 — work5 positions
Nov 2025 — Present
Glasgow · Remote

Forward Deployed AI Engineer · Kodamai

Primary technical partner for enterprise clients across multiple simultaneous engagements. Current projects: Madfo3 (accounts payable automation with LangGraph + SAP/ERP), Nazir (ANPR with active learning — YOLOv11 detection + Qwen3-VL verification on edge), a 20TB document intelligence pipeline on GCP Vertex AI using Qwen3-VL and layout-aware embeddings served through vLLM, and a warehouse logistics automation system with SAP HANA integration.

LangGraphSAP HANAGCP Vertex AIQwen3-VLYOLOv11vLLM
Jul 2024 — Nov 2025
Singapore · Remote

Lead Data Scientist · Exora AI

Designed and shipped a multi-agent clinical AI assistant serving live physician workflows. Full stack: LangGraph orchestration, RAG with hybrid vector search, Whisper STT, multimodal embeddings (SigLIP-2 + InternVL3) for clinical imagery, PHI-compliant observability across 7 microservices, and evaluation tracking clinical competency across model versions. Reduced LLM latency 35% and cost 25%. Passed HIPAA + Singapore PDPA audit.

LangGraphRAGWhisperSigLIP-2InternVL3HIPAA
Jan — May 2024
Toronto · Remote

Senior ML Engineer · The Quell App

Sole engineer on a pre-seed health & wellness startup. Shipped the full GenAI product in 6 weeks. Evaluated 8+ LLMs, fine-tuned Mistral-7B with LoRA, built self-hosted inference with quantized serving, safety guardrails, and an A/B evaluation framework. Demo contributed to closing a $250K pre-seed round.

Mistral-7BLoRAGuardrailsvLLM
Apr 2023 — Jan 2024
Islamabad, PK

ML Engineer · DiveDeepAI

Built a production price-intelligence pipeline: XGBoost models at ~12% MAPE, ~5,000 products/week at 90%+ accuracy on AWS EC2 with FastAPI, PostgreSQL, Redis, Celery. Feature engineering across tabular and scraped data. ~99% uptime.

XGBoostFastAPICeleryAWS
Jan 2022 — Apr 2023
Remote

ML Engineer · Upwork (Top Rated)

Delivered LLM fine-tuning (LoRA, QLoRA), NLP pipelines, computer vision systems (YOLOv5, CNN architectures, segmentation), and classical ML / forecasting for international clients across diverse domains.

LoRAQLoRAYOLOv5Forecasting
03 — open source30+ merged PRs

Patches in core ML & language infrastructure. All work in my own time.

@mohsinm-dev
contribution graph · last 12 months
734
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google/flax
#5 · last 12 months

Top contributor on the official leaderboard. 20 commits, +1,075 / −506. Merged PRs in Flax NNX core: PR #4891 fixed nnx.tabulate crash with empty/None values. PR #5008 fixed variable hook display bugs. Both shipped in v0.12.1.

#4891#5008
★ 6.4k
Python
python/cpython
6 merged PRs

PR #141331 fixed TextIOWrapper.tell() assertion failure with standalone carriage return (backported to 3.13 + 3.14). PR #144696 corrected re.Match.group() doc on [1..99] range. Also documented asyncio Task cancellation propagation and inaccurate object-comparison docs.

#141331#144696+4
★ 63k
C / Python
uber/causalml
3 merged PRs

PR #850 fixed estimation_sample_size not propagating to individual trees in UpliftRandomForestClassifier. PR #878 fixed ValueError on read-only arrays in BaseSLearner.predict(). PR #879 fixed seed parameter TypeError in BaseDRLearner bootstrap CI.

#850#878#879
★ 5.1k
Python
04 — research interests

These grew out of problems I keep running into in production systems.

01 / reliable

Reliable agentic systems

How to build agents that know when to act and when to escalate. Most agents are overconfident about their own success rates — this matters more in regulated domains like healthcare and finance, where a wrong autonomous action has real consequences.

02 / evaluation

Evaluation for AI systems

Offline benchmarks (MMMU, MathVista, MMStar, DocVQA) often do not predict production quality. I am interested in evaluation methods that catch what actually matters: user outcomes, business metrics, and real-world failure modes that benchmarks systematically miss.

03 / llm

LLM efficiency & quantization

Quantization (AWQ, GPTQ, GGUF, FP8), context compression, distillation, and SLM routing for deployment under strict latency, cost, or data-residency constraints. Currently exploring how quantization affects reasoning quality in non-English languages — Urdu specifically — where the damage is often invisible to standard automatic metrics.

04 / vision-language

Vision-language models in applied settings

How well current VLMs (Qwen3-VL, GLM-4.6V, InternVL3.5, Llama 4 multimodal, Gemini 3) actually generalize to messy real-world data: medical imagery, scanned documents, surveillance feeds, long-form video. Particular interest in long-context multimodal reasoning at 256K+ tokens and the gap between benchmark scores and production reliability.

05 / multilingual

Multilingual & low-resource LLMs

Building systems that work reliably for speakers of non-Latin script languages, where current models, tokenizers, and evaluation infrastructure are weakest. Particular interest in how training choices (tokenization, data mixture, instruction tuning) propagate into downstream failure modes.

05 — skills
Languages
PythonC++BashTypeScript (working)
Core ML & DL
PyTorchJAX / Flax (NNX)scikit-learnXGBoostLightGBMCausal inferenceUplift modelingDR / S / T-learnersTime-seriesBayesian
Vision-Language
Qwen3-VLGLM-4.6VInternVL3 / 3.5Llama 4 MMMiniCPM-VMolmoKimi-VLGemma 3PaliGemma 2DeepSeek-VL2Tarsier2Gemini 3 VisionGPT-5 Vision
Vision & CV
SigLIP-2DINOv3CLIPEVA-CLIPYOLOv8 / v11SAM 2Grounding DINODonutLayoutLMv3NougatPaddleOCRdots.ocrANPRSegmentation
LLMs & Agents
LangChainLangGraphPydanticAIMCP serversMulti-agent orchestrationRAGHybrid searchRAGASPrompt eng.Tool use
Fine-tuning & Efficiency
LoRAQLoRADoRASFTRLHFDPOGRPODistillationAWQGPTQGGUFFP8HF TransformersAxolotlUnsloth
Inference & Serving
vLLMSGLangTensorRT-LLMllama.cppOllamaTritonBentoMLKServe
Multimodal Evals
MMMUMathVistaMathVisionMMStarDocVQAChartQAOCRBenchVideo-MMECustom prod evals
Infrastructure
FastAPIPostgreSQLpgvectorRedisKafkaDockerKubernetesCI/CDGCP Vertex AIAWSMLflowW&B
Compliance
HIPAASingapore PDPAPHI / PII redactionAudit trail design
06 — education
2019 — 2023

B.Sc. Computer Science

Capital University of Science and Technology · Islamabad
07 — contactusually < 24h

Working on something careful? Write to me.

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