AI/ML Engineer · Data Scientist — production RAG agents, LLM fine-tuning & serving, MLOps, and rigorous statistical evaluation.
I build production-grade AI systems that bridge research and deployment — stateful RAG agents, QLoRA fine-tuning pipelines, real-time drift monitoring, and statistically sound model evaluation. My work favors honest benchmarks, maintainable code, and observable systems.
- 🔭 Currently: production RAG & agents, QLoRA fine-tuning, MLOps & drift detection
- 🧪 Specialties: LLM/VLM evaluation, statistical inference, data engineering
- 🏭 Domains: Industrial AI, NLP, anomaly detection, reinforcement learning
- 🎯 Open to: AI Engineer · ML Engineer · Data Scientist · Research roles
On this portfolio: every project README reports only what its code and committed artifacts can actually prove. Real outputs are cited as numbers; scaffolds and blueprints are labeled as such. No inflated metrics.
| Project | What it is | Stack |
|---|---|---|
| Production RAG + Human-in-the-Loop Agent | Stateful agent for industrial document intelligence — LangGraph state machine with a human approval gate (retrieve → draft → grounding-check → approve → finalize/abstain), pgvector retrieval, durable checkpointing, Prometheus metrics, and a Postgres audit trail. MIT-licensed, strictly typed. |
LangGraph · pgvector · FastAPI · Prometheus |
| QLoRA → Quantize → Serve | End-to-end 7B fine-tuning pipeline: QLoRA (4-bit NF4) → eval → merge → AWQ → vLLM serving. Config-driven, reproducible, transparent eval harness (GPU benchmarks marked pending — no placeholder numbers). | PEFT · TRL · bitsandbytes · vLLM |
| Vectorless RAG Lab | Research harness comparing 7 embedding-free retrieval pipelines — tree-navigation, BM25, agentic search, a hybrid RRF router, quote-extraction, and a novel three-stage hybrid. Local-first LLM client, telemetry, and a RAGAS / LLM-as-judge eval scaffold. | BM25 · RAGAS · Ollama |
| AI for Business — 3 Case Studies | Churn, energy forecasting & segmentation with reproduced results: churn ANN ≈ 84% test accuracy, energy demand Random Forest R² ≈ 0.68, segmentation silhouette ≈ 0.41 (k=2). | TensorFlow · scikit-learn · pandas |
| Bayesian LLM/VLM Evaluation | Goes beyond point estimates: partial-pooling logistic model (correct ~ system + (1|field) + (1|doc_class)) via Bambi/PyMC, posterior contrasts with 94% HDIs, ArviZ diagnostics & LOO. |
PyMC · Bambi · ArviZ |
| Doc Extraction Benchmark (VLM vs OCR) | Honest, pre-registered field-level extraction benchmark on CORD — Pixtral vs Tesseract/EasyOCR, normalized exact-match scoring, document-clustered bootstrap CIs + exact McNemar tests. | Pixtral · Tesseract · pandera · SciPy |
Focused, production-shaped building blocks — each a compact but working implementation.
| Repo | What it does | Stack |
|---|---|---|
| data-quality-framework | Schema / null / range / freshness / SLA validators with HTML reports & alerts | pandas · pytest |
| airflow-etl-pipeline | Production ETL DAGs: CSV→Postgres, API ingest, dbt trigger, Slack alerts | Airflow |
| dbt-analytics-models | Staging → intermediate → mart models with schema tests & freshness SLAs | dbt · SQL |
| kafka-event-streaming | Idempotent producers, manual-commit consumers, DLQ, replay, lag monitor | Kafka |
| spark-streaming-kafka | PySpark Structured Streaming: watermarked windowed aggregations + JDBC sink | PySpark |
| postgres-data-modeling | Star schema, range partitioning, BRIN/partial indexing, Alembic, pgTAP | PostgreSQL |
| Repo | Focus |
|---|---|
| mlops-drift-detector | Real-time data/concept drift — PSI + Page-Hinkley, streaming monitor, Prometheus exporter |
| moon_lander_rl | Deep RL — DQN agent (PyTorch) on LunarLander-v3, with checkpoints, rollout videos & training curve |
| ALS_Disease_Severity | Clinical ML — ALSFRS-R severity stratification from biomarker/functional features |
| OCR_Vision_Model_for_Industries | Modular industrial OCR framework — ensemble OCR + LLM verification, CER/WER metrics |
| genai-eval-framework | LLM eval harness — parallel MMLU evaluator, Anthropic/Ollama adapters, on-disk caching |
Languages & Core
LLM & GenAI
Data & MLOps
Building production AI systems that work in the real world · Open to AI/ML Engineering, Data Science & Research opportunities · linkedin.com/in/fahil-ejaz