Available for select projects

I take AI from demo to production.

Founders and CTOs bring me AI products that need to actually work: reliable, fast, safe, and cost-controlled. The demo is the easy part. I build the 80% that makes it real.

Send a project inquiry Try my work, live
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Remote-first · Lahore, PK (UTC+5) · Partnering with founders worldwide

12% → 34%

lead conversion, outreach AI

12 h → 45 min

enterprise reporting

5.4× cheaper

self-hosted inference

30+

AI systems shipped

The 80% that makes it real

A demo isn’t a product.

Anyone can get a model working in the happy path. Making it stay reliable, fast, safe and affordable under real traffic is the hard 80%, and it’s where most AI projects stall. That is the part I build.

Your prototype

Impresses in the demo. Then it breaks under real users, cost and edge cases.

The 80% I build

Serving (vLLM) Evaluation harness Observability Guardrails & safety Cost control

In production

Reliable, fast, safe and cost-controlled, a system your team can run and trust.

Case studies

Selected work

Problem, approach, measurable impact. Every case links to a live demo you can run.

Technology Digest

2026 to present

Production AI for a mental-health platform

Problem. Sole ML engineer on a safety-critical product with zero ML infrastructure, where a wrong answer has real consequences.

Approach. Built 12 production systems from scratch: LLM serving with vLLM behind a stable API, a multi-tier real-time crisis-detection cascade, WHO/NICE-aligned clinical note generation, multilingual semantic search, and LLM intent routing, with adversarial safety evaluation and permanent regression harnesses.

>99% of traffic resolved in <100 ms~99.7% crisis-detection accuracy5.4× cheaper than external APIs
Read the case study Try the safety cascade live

TransData

2023 to 2026

Multi-agent legal Q&A for enterprises

Problem. Enterprises needed trustworthy LLM answers over their own documents and tools.

Approach. Multi-agent orchestration (LangGraph, LangChain, CrewAI) over GPT-4, Llama, Mistral and open models; RAG with hybrid retrieval, contextual compression and multi-stage verification; enterprise integrations (Okta, Jira, Databricks, SharePoint) on AWS.

92% accuracy in legal Q&A40% fewer support tickets89% user satisfaction
Read the case study Try contract analysis live

TransData

2024

AI cold-outreach automation

Problem. Manual lead qualification was slow and converted poorly.

Approach. Automated pipeline over Apollo lead data with custom ICP scoring and GPT-4 few-shot personalized emails, plus an admin dashboard with CRM integration.

Conversion 12% → 34% (+183%)90% less manual qualification2.3× deal closure
Read the case study Try lead scoring live

TransData

2024 to present

Agent-based BI report generation

Problem. Business reports took ~12 hours of manual analysis per cycle.

Approach. LangGraph workflow with specialized agents for extraction, comparative analysis, insight generation and report compilation across CSV, PDF, Word and Excel, deployed on AWS with monitoring.

12 hours → 45 minutes (-94%)3× reporting capacity
Read the case study Try the report generator live

Live AI Lab

Don’t take my word for it. Run the AI yourself.

Working demos of the systems I build, organized by industry. Every demo shows recorded results instantly, no sign-up and no API key needed. Plug in your own OpenAI or OpenRouter key for live runs; it stays in your browser for the session and is never stored.

Retrieval

RAG Playground

Ask questions over documents and watch it retrieve, then answer only from what it found, with sources. The core of RAG you can trust.

Open demo → Document AI

Invoice → JSON

Turn a messy invoice into clean, accounting-ready structured data with risk flags. The highest-ROI AI use case, running live.

Open demo → Customer experience

Policy-Grounded Support Reply

A customer message plus your policy in, an accurate on-brand reply out. Automate support without hallucinations.

Open demo →
Explore all demos Or browse by use case →

Services

What I do

AI consulting for founders, CTOs and startup teams shipping AI products. A demo is easy, production is a systems problem.

Production AI & MLOps

Take models out of notebooks: serving, eval harnesses, observability, latency and cost optimization, CI/CD, monitoring. The model is 20% of the work; I build the other 80%.

Agentic AI, RAG & LLM orchestration

Multi-agent systems (LangGraph, CrewAI), RAG users can trust with hybrid retrieval, re-ranking and strict grounding, fine-tuning, and agents only where they earn it.

Safety-critical & high-stakes AI

Crisis and guardrail systems, adversarial evaluation, content moderation, bias testing. For healthcare, finance and any domain where a wrong answer has real consequences.

AI advisory & cost optimization

Build-vs-buy analysis, model selection across GPT, Llama and open models, token budgets, self-hosting economics. I have cut inference costs 5 to 10× for teams.

Stack: LangGraph · LangChain · CrewAI · GPT · Llama · Mistral · PyTorch · vLLM · ChromaDB · Faiss · FastAPI · Docker · Kubernetes · AWS · GCP

Process

How we work together

Low-risk from the first call. You see working software every week.

1

Intro call

Free 30 minutes. You describe the problem; I tell you honestly whether AI is the right tool and what it would take.

2

Scoped discovery

A short paid discovery: I map your data, constraints and risks, and deliver an architecture plan with cost estimates you can execute with or without me.

3

Build with weekly demos

Iterative delivery with a working demo every week, evaluation baked in from day one, and a handover your team can maintain.

Engagements

Ways to work together

Fixed-scope and low-risk. Most partnerships start with a Discovery Sprint, then continue only if it makes sense.

AI Discovery Sprint

Fixed fee · ~1 week

Best if you're not yet sure what to build, or whether AI is even the right tool.

  • ✓Data, constraints and risk assessment
  • ✓Architecture plan and model/build-vs-buy recommendation
  • ✓Cost and timeline estimates

Outcome: A concrete plan you own and can execute with or without me.

Most common

Build Engagement

Project-based · scoped from the Sprint

Best for taking a prototype to a reliable production system.

  • ✓Iterative build with a working demo every week
  • ✓Evaluation, observability and safety baked in
  • ✓Deployment, cost optimization and a maintainable handover

Outcome: A production system your team can run and trust, not a demo.

Fractional / Advisory

Monthly retainer

Best if you have a team and need senior ML judgment on tap.

  • ✓Architecture and code review
  • ✓Build-vs-buy and cost-optimization guidance
  • ✓Hiring support and on-call technical direction

Outcome: Senior ML leadership without a full-time hire.

Not sure which fits? Book a free intro call and I'll point you to the right one, even if that's "you don't need me yet". Send an inquiry or .

Recognition

Proof, not promises

Cited 46×

Peer-reviewed research

Computers in Biology and Medicine (Elsevier, IF 7.7). First to propose face-region-aware pooling for BMI estimation.

Read the paper →

10,000+

LinkedIn followers

Founders, CTOs and engineers reading my work

2× Medalist

Academic distinction

Silver Medalist MS CS (ITU) · Gold Medalist BS CS

Speaker

Invited industry talk

NUML Lahore: turning LLM ideas into production systems

The engineer behind the systems

About

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 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 for legal Q&A, outreach automation and agent-based reporting.

“The teams that move fast don’t skip the thinking. They just learned to ask "what breaks this?" before "how do I build this?"”

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)

From my LinkedIn

Writing & insights

How I think about shipping AI, read by 10,000+ founders, CTOs and engineers.

Read all notes
MLOps

LLM benchmarking on constrained hardware is archaeology, not a spreadsheet

I scheduled a clean benchmark of 5 candidate LLMs. Two could not even load, one had unpredictable latency, and the biggest was not worth it. Why you must know your GPU's real limits before the run, not after.

Read the note → AI safety

Safety that only works in English isn't safety

For 3 months, non-English users were invisible to a mental-health app's safety system. How an English-first toxicity classifier and a keyword crisis detector missed non-English suicidal ideation, and the multilingual fix.

Read the note → AI safety

Peer voice vs expert voice: a one-line prompt change that was a clinical decision

In sensitive contexts, tone is not cosmetic. Peer support and expert guidance work through different psychological mechanisms, so choosing the voice is domain judgment, not styling.

Read the note →

Also on LinkedIn, where 10,000+ founders, CTOs and engineers follow along.

FAQ

Questions founders ask first

How do you price projects?

Low-risk and transparent: a free 30-minute intro call, then a short fixed-fee discovery sprint where I map your data, constraints and risks and hand you an architecture plan with cost estimates you own. Build work is project-based and scoped from there, no open-ended hourly surprises.

What if AI isn’t the right solution for us?

I’ll tell you. Part of the value is an honest read on the intro call, sometimes the right answer is a simpler system, or not to build with AI at all. I’d rather keep a reputation than sell you a project that won’t work.

Do you build production systems or just demos?

Production. A demo is the easy 20%; I build the 80% that makes it reliable, fast, observable, safe and cost-controlled under real traffic. 30+ shipped systems, not prototypes, and you can run many of them live in the AI Lab on this site.

Can you handle sensitive or regulated data?

Yes, it’s my sharpest edge. I’m the sole ML engineer on a safety-critical mental-health platform, with real-time crisis detection, PHI de-identification and adversarial safety evaluation. For regulated data I default to privacy-first design: on-device where possible, strict grounding, and audit trails.

Can you work with our existing team and stack?

Yes. I integrate with your infrastructure and tools, write clean contracts and documentation, and hand over systems your team can actually maintain. I’ve worked as both the sole engineer building from zero and inside multi-team enterprise environments.

What’s your availability and how do we work across time zones?

I take a limited number of engagements so each gets senior attention, and I’m remote-first from Lahore (UTC+5), partnering with founders and teams across the US and EU with solid working-hour overlap. Tell me your timeline on a call and I’ll be honest about fit.

Building AI and want it to actually ship?

Tell me what you’re building and where it’s stuck. I’ll reply within one business day with an honest read on whether I can help.

Email me: nadeem@nadeemyousaf.dev Message on LinkedIn

Not ready to email? . It answers instantly, 24/7.