Production-ready RAG, AI agents and ML systems that move real business metrics
I've spent 9+ years doing just that
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Is your RAG prototype giving confident answers that turn out to be wrong?
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Do you need an LLM evaluation framework before you put AI in front of customers?
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Are your models stuck in notebooks instead of running in production?
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Want ML that ties directly to revenue, like lead scoring, forecasting or churn prediction?
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Need someone who can explain the results to executives, not just engineers?
About me
Hi! I'm MD Farooque, a Senior Data Scientist based in Delhi NCR, India. I help businesses turn data and AI into systems that run reliably in production and pay for themselves.
Over the past 9+ years I've taken ML solutions from raw data all the way to production: ingesting and wrangling data at scale with Python, SQL, PySpark and BigQuery, building statistical and machine learning models, and serving them through APIs on Google Cloud, with exposure to AWS and Azure as well. I standardized my team's ML engineering practice on MLflow, CI/CD and drift monitoring, so models stay reproducible and healthy long after launch.
More recently I've focused on generative AI. I built the LLM/RAG evaluation framework that helped secure enterprise AI contracts with Stanford, MIT and Berkeley, and shipped AI Tutor, a production RAG service that answers questions from learners on live university courses.
I'm also IEEE-published, and I enjoy translating complex analytical findings into clear decisions for executives and clients.
How I can help
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RAG & AI Agents
Retrieval-augmented generation systems built for production: document parsing, chunking, embeddings, vector search and prompt design, served via API with answer quality balanced against latency and cost.
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LLM Evaluation & Guardrails
Evaluation frameworks with retrieval and generation metrics, automated validation gates and hallucination-risk controls, so you can prove your AI is trustworthy before it reaches users.
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Predictive Machine Learning
Lead scoring, profitability and demand forecasting, churn prediction and segmentation, built with rigorous feature engineering, cross-validation and A/B testing so the numbers hold up.
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MLOps & Production Engineering
MLflow experiment tracking and model registry, CI/CD, drift and performance monitoring, and automated retraining on GCP, so your models keep delivering after launch day.
Why work with me?
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Business Impact First
My work is measured in outcomes: a lead-scoring model that drove more than $1M in business impact, and a search-signal data pipeline that generated $350,000 in organic conversion value.
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Proven in Production
I don't stop at the prototype. My RAG and ML systems run in production, serving real users on live university courses and feeding decisions in day-to-day operations.
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Educator & Communicator
I've co-authored Data Science and ML curriculum aligned with US industry standards, so I know how to explain complex methods clearly to non-technical stakeholders.
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Rigorous & Research-Driven
I'm an IEEE-published researcher and a hackathon winner (churn prediction). Hypothesis testing and experimental design are built into how I work.
Experience
Senior Data Scientist, Emeritus
- Designed and deployed an enterprise predictive lead-scoring model in PySpark on GCP. The top 30% of scored leads capture 85% of conversions, driving more than $1M in business impact.
- Engineered the LLM/RAG evaluation framework that secured enterprise AI contracts with Stanford, MIT and Berkeley.
- Built and shipped AI Tutor, a LangChain-orchestrated RAG service serving learners on live university courses.
- Standardized the team's ML workflow on MLflow and CI/CD, with drift monitoring and automated retraining.
Data Scientist, Great Learning
- Designed a predictive profitability model for US-market programs, increasing the number of profitable launches by 16%.
- Established a hypothesis testing and experimental design framework for business interventions.
- Built real-time performance-monitoring dashboards in SQL and Tableau for financial KPIs and ML model health.
- Co-authored Data Science and ML curriculum that drove a measurable NPS increase for US launches.
Machine Learning Engineer, SynergyLab
- Deployed ML and deep learning computer vision models for industrial applications, focused on scalability and real-time inference.
- Automated feature engineering workflows for large-scale datasets, cutting manual data preparation time by 40%.
Education: Bachelor of Technology (B.Tech), MIT, Pune University
Awards & publications: Winner, Emeritus Internal Hackathon (Churn Prediction Model) · Research paper in Machine Learning, published by IEEE
Tech stack
What my colleagues say about my work
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Siddharth Shrotriya
Author, Researcher and Data Science Trainer · managed MD directly
"Hardworking professional and can become an essential part of your Analytics team."
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Sayyam Jain
7+ years in AI and Leadership · worked with MD on the same team
"Farooque is an excellent co-worker. While at Jazari, we worked on many research projects including unsupervised facial recognition. Through the combined effort we achieved a very good accuracy which was presented and published in IEEE Conference."
Frequently asked questions
What kinds of projects do you take on?
I focus on four areas: RAG systems and AI agents, LLM evaluation and guardrails, predictive machine learning (lead scoring, forecasting, churn, segmentation), and MLOps for getting models into production and keeping them healthy. If your problem sits somewhere between data and a business decision, it's probably a good fit.
What industries do you have experience in?
Most of my recent work is in EdTech and higher education, including AI products used by learners on Stanford and MIT courses. I've also built marketing and growth analytics (lead scoring, search-signal monetization, conversion optimization) and industrial computer vision systems. The methods transfer well across sectors.
Which tools and cloud platforms do you work with?
My primary production environment is Google Cloud (BigQuery, GCP ML tooling), and I also work with AWS and Azure. Day to day I use Python, SQL, PySpark, Databricks, LangChain, vector search, MLflow and CI/CD. I'm happy to work within your existing stack.
Can you help if we already have a RAG prototype that isn't working well?
Yes. That's one of the most common situations I see. The fix is usually upstream of the model: document parsing, chunking and evaluation. I start by measuring where quality is being lost, then fix the highest-leverage stage first. I wrote about this in detail in my blog post on why parsing is the RAG layer you're underfunding.
How do you handle data security and confidentiality?
I'm happy to sign an NDA before we discuss details, and I can work inside your own cloud environment and access policies so data doesn't leave your infrastructure. For LLM projects I build in controls such as PII scrubbing before text reaches logs, embeddings or the model.
How do we get started?
Book a free intro call. We'll talk through your goals, your data and what success looks like. From there I'll suggest a scoped first step, often a small pilot, so you can see results before committing to anything larger.
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Let's have a virtual coffee together!
Want to see if we're a match? Let's have a chat and find out. Schedule a free 30-minute session to discuss your data and AI challenges and explore how we can work together.