I take AI from demo to production.
I help founders ship AI that actually works in production — reliable, fast, and cost-controlled. The demo is the easy part. I build the 80% that makes it real.
Years experience
AI/ML systems shipped
Cheaper inference
Legal Q&A accuracy
Remote-first · Partnering with founders worldwide · Based in Lahore, PK (UTC+5)
The engineer behind the systems
I'm a Senior ML Engineer who builds reliable, well-engineered GenAI systems that hold up in the real world. With 7+ years across LLMs, agents, RAG, MLOps, computer vision, and NLP, I've shipped 30+ AI/ML systems — from research labs to enterprise automation to safety-critical healthcare.
Today I'm the sole ML engineer designing and building the full ML stack for a healthcare AI platform — infrastructure, multi-agent workflows, real-time safety systems, and cost optimization, all from zero. Before that, at TransData, I built multi-agent enterprise systems delivering legal Q&A at 92% accuracy, outreach automation that lifted lead conversion from 12% to 34%, and agent-based reporting that cut a 12-hour process to 45 minutes.
“The teams that move fast don't skip the thinking. They just learned to ask "what breaks this?" before "how do I build this?"
Nadeem Yousaf
Senior ML Engineer
Currently
Sole ML Engineer, healthcare AI platform
Focus
Production AI · LLMs · Agents · RAG · MLOps
Education
MS CS — Silver Medalist (ITU) · BS CS — Gold Medalist
Languages
English (professional) · Urdu · Chinese (basic)
For founders and CTOs shipping AI products
Take models out of notebooks: serving, eval harnesses, observability, latency/cost optimization, CI/CD, monitoring. The model is 20% of the work; I do the other 80%.
Multi-agent systems (LangGraph, LangChain, CrewAI, N8N), RAG that users can trust (hybrid retrieval, re-ranking, grounding), prompt engineering, fine-tuning.
Adversarial evaluation, crisis/guardrail systems, build-vs-buy cost modeling. For domains where a wrong answer has real consequences.
A demo is easy. Production is a systems problem. Here's what consistently blocks teams — and what I build instead.
Hybrid retrieval, re-ranking, metadata filters, and strict grounding boundaries — so answers don't hallucinate or freewheel.
Test sets, regression checks, groundedness & faithfulness metrics. A hallucination that "looks confident" is still a failure.
End-to-end tracing, hallucination-rate and quality-drift monitoring, p95 latency and cost-per-task — so you fix systems, not guess.
Agents where they create leverage; deterministic pipelines and guardrails where uncontrolled reasoning raises risk.
Case studies: Problem → Approach → Impact
2026 – present
Problem
Joined as the sole ML engineer with zero ML infrastructure on a sensitive, high-stakes product where a wrong answer has real consequences.
Approach
Built 12 production systems from scratch on a single GPU: an LLM serving + MLOps stack (vLLM, OpenAI-compatible API, token streaming, context compaction) behind a stable contract layer; a multi-tier real-time content-safety & crisis-detection cascade (regex → lightweight classifier → LLM); structured clinical-note generation (WHO/NICE-aligned); offline multilingual semantic search; and LLM-classifier intent routing. Led adversarial safety evaluation across 200+ scripted clinical-QA turns with permanent regression harnesses.
Impact
2023–2026
Problem
Enterprises needed trustworthy LLM answers over their own documents and tools.
Approach
Multi-agent systems (LangGraph, LangChain, CrewAI) orchestrating GPT-4, Claude 3.5, Llama, Mistral; RAG with ChromaDB/Faiss, contextual compression, multi-stage verification; OAuth2/Okta, Jira/Databricks/SharePoint integrations; Dockerized on AWS EC2.
Impact
2024
Problem
Manual lead qualification was slow and low-converting.
Approach
N8N workflow processing Apollo CSVs with dedup/validation, custom ICP scoring, GPT-4 few-shot personalized emails; React + Material-UI admin dashboard with CRM integration.
Impact
2024–present
Problem
Business reports took ~12 hours of manual work.
Approach
LangGraph workflow with specialized agents for extraction, comparative analysis, insight generation, and report compilation across CSV/PDF/Word/Excel; charts via Matplotlib/Plotly; Dockerized on AWS EC2 with monitoring + email delivery.
Impact
2021–2022
Problem
Real-time public-safety analytics from CCTV.
Approach
Object detection/tracking (YOLO v3–v7, Detectron2, ByteTrack, FairMOT), face recognition (YOLO + DSFD low-light + fine-tuned FaceNet), parking/boundary/loitering detection, anomaly detection, OCR, image enhancement.
Impact
2021
Problem
Existing face-to-BMI methods missed detailed local facial information.
Approach
Semantic-segmentation-based region-aware pooling (Reg-GAP) on FaceNet/VGG-Face features.
Impact
I build production-grade AI systems end-to-end. From concept to deployment.
Build AI features from scratch: chatbots, agents, search, document processing. Multi-agent workflows, RAG pipelines, fine-tuned models.
Design scalable AI infrastructure: model serving (vLLM), eval harnesses, safety gates, observability. Production-ready from day one.
Automate workflows with multi-agent AI: lead scoring, content generation, document analysis, customer support. N8N orchestration + LLM intelligence.
Build vs buy analysis, token budget optimization, model selection (GPT/Claude/Llama), deployment strategy. Reduce costs by 5-10x.
Content moderation, crisis detection, adversarial testing, bias evaluation. Critical for healthcare, finance, customer-facing AI.
Take your AI prototype to production: infrastructure setup, monitoring, CI/CD, multi-region deployment, performance optimization.
The toolkit behind shipped, production-grade systems.
Proof, not just promises.
10,000+
LinkedIn followers
A growing audience of founders, CTOs & engineers
Published
Peer-reviewed researcher
Computers in Biology and Medicine (Elsevier, IF 7.7)
Read the paper2× Medalist
Academic distinction
Silver Medalist MS CS (ITU) · Gold Medalist BS CS
Speaker
Invited industry talk
NUML Lahore — turning LLM ideas into production systems
Certified
DeepLearning.AI
Neural Networks & Deep Learning · CNNs
22.4%
Research improvement
Reg-GAP gain on VIP-attribute (Face-to-BMI)
I share how I think about shipping AI with an audience of 10,000+ founders, CTOs & engineers.
90 days, zero infrastructure, a problem that couldn't fail. Why the first question isn't "what do I train?" — it's "what does failure look like?"
What consistently blocks teams from shipping GenAI — RAG you can trust, evaluation before scale, LLMOps & observability, and agents only where they earn it.
M = (P × 4B / (32/Q)) × 1.2 — calculate the VRAM you need for any model and quantization, with worked Llama-2-70B examples.