Hargurjeet
Singh Ganger
Principal GenAI Architect & Data Scientist
I bridge the gap between proof-of-concept AI models and resilient, production-grade Generative AI architectures. Specializing in deploying high-fidelity enterprise RAG systems, orchestrating autonomous multi-agent workflows, and implementing robust MLOps guardrails that ensure safety, accuracy, and performance at scale.
70%
Reduction in manual document extraction time via production LLM pipelines.
30%
Cut in oil refinery maintenance costs through predictive modeling.
30%
Increase in Value-Added Service sales using market-basket models.
90%+
Accuracy in multimodal document processing on 100K+ PDFs.
What I Can Solve
Scalable RAG pipelines
Design and ship hybrid retrieval systems (BM25 + dense vectors + reranker) that process 100K+ multimodal documents with 90%+ accuracy in production.
Agentic AI workflows
Architect multi-agent systems with CrewAI and LangGraph — tool-augmented, guardrailed, and evaluated with Ragas before they touch production.
LLM evaluation frameworks
Build frameworks that measure faithfulness, relevancy, hallucination, toxicity, and bias — so model upgrades don't silently degrade your product.
Research → production AI
Take proof-of-concept LLM experiments and harden them: containerise, add CI/CD gates, instrument with latency and drift monitoring on AWS.
AWS Bedrock & GenAI infra
Deploy secure, cost-efficient GenAI systems using Bedrock, Textract, OpenSearch, and Step Functions — with CloudWatch observability built in.
ML-driven business outcomes
Translate messy enterprise data into XGBoost, Random Forest, or deep-learning models that move real metrics: 30% fewer maintenance incidents, 10% budget saved.
From IT Analyst to AI Systems Builder
At British Telecom I'm leading the Generative AI charge — deploying RAG-powered chatbots on AWS Bedrock, building multi-agent workflows with CrewAI, and designing LLM evaluation frameworks that catch hallucinations before they reach production. My focus is always the same: AI that works in the real world, not just the notebook.
Before that, at Royal Dutch Shell, I moved into data science — building predictive maintenance models that cut equipment downtime by 25% across oil refineries, and forecasting dashboards that saved 10% of budget allocations across five geographies. That's where I fell in love with the gap between a working model and a working solution.
I started my career at TCS testing point-of-sale systems, spending a year in the UK guiding offshore teams through complex software rollouts. Those early years taught me how enterprise systems break under real conditions — a foundation that still shapes how I build today.
Work Experience
Senior Data Scientist
May 2022 – PresentBritish Telecom (BT) · Bangalore, India
- ▸Architected and led delivery of an enterprise-grade conversational AI system (LLMs + RAG), reducing manual document extraction time by 70% while processing 100K+ files with 90%+ accuracy via AWS Bedrock and OpenSearch.
- ▸Designed and deployed multi-step agentic workflows using CrewAI and LangGraph, integrating JSON schema validation, retry loops, and custom hallucination guardrails in production.
- ▸Developed an LLM evaluation framework using Ragas and LLM-as-judge pipelines to assess faithfulness, toxicity, bias, and hallucination detection.
- ▸Built an automated email intelligence pipeline processing 6,000+ weekly escalation emails, fine-tuning a LLaMA-2 7B model locally via QLoRA for a 40% F1-score improvement.
- ▸Engineered recommendation systems (Random Forest + XGBoost) and market basket analysis (Apriori) increasing SD-WAN sales by 10% and Value-Added Services (VAS) sales by 30%.
Data Scientist
Sep 2016 – May 2022Royal Dutch Shell · Bangalore, India
- ▸Developed and evaluated predictive maintenance models (XGBoost, Random Forest) using SHAP-based interpretability and ROC-AUC scoring, cutting equipment maintenance costs by 30% and unplanned downtime by 25%.
- ▸Engineered end-to-end data pipelines in Python (Pandas, NumPy) and developed Power BI dashboards to forecast materials on-time delivery across 5 geographies, saving 10% budget.
- ▸Acquired 5+ years of experience with data warehousing, ETL pipelines, big data analytics, and relational databases.
IT Analyst
Dec 2010 – Aug 2016Tata Consultancy Services (TCS) · India / UK
- ▸Performed System Integration Testing (SIT) and User Acceptance Testing (UAT) to validate client Point-of-Sale (PoS) systems at enterprise scale.
- ▸Spent one year in the UK onsite guiding offshore teams through the implementation of new PoS software.
- ▸Acquired extensive experience working with card and payment systems, PCI standards, and ISO 8583 protocols.
Featured Projects
A carefully curated selection of deep-dive AI engineering projects, spanning autonomous agents, production RAG pipelines, and local SLM benchmarks.
Antigravity: Autonomous UI Designer
- Closed-Loop Critic: Triggers autonomous redraw cycles by validating generated mockup images against guidelines.
- Evaluator Scoring: Computes quantitative metrics for brand consistency, color alignment, and layout accuracy to score each generation attempt.
- State & Memory: Manages agent context and state retention across iterations using a central memory store to refine subsequent image generation.
- Robust Guardrails: Enforces strict boundaries via JSON schema validation, retry loops, and degrade-gracefully fallbacks via pre-trial groups.
- Streaming Timeline: Traces and displays the agentic step-by-step cognitive thoughts and evaluations alongside intermediate drawing cycles.
- Google Agentic Systems: Orchestrates multimodal Gemini 2.5 and Imagen 4 Ultra models to analyze briefs, map brand DNA, and generate high-fidelity images.
Local Multi-Agent Folder Organizer
- Hierarchical Coordinator-Specialist Architecture: Configures a lead orchestrator agent that partitions folder listings into subtask categories, preventing context window limits.
- Multi-Agent Concurrency: Spawns category-specific specialist subagents in parallel using a Python `ThreadPoolExecutor` to slash local Ollama inference latency by 60%.
- Pydantic Output Validation: Enforces strict JSON schemas on local SLMs using CrewAI's output parsing, guaranteeing zero formatting errors.
- Human-in-the-Loop Safe Gate: Implements a CLI preview table and user confirmation prompt before mutating any folder structure, supporting a non-destructive dry-run mode.
- Transactional Rollback Log: Records all file migrations atomically in a central `history.json` transaction log, facilitating instant programmatic recovery.
Local AI Assistant & SLM Benchmarking
- Local SLM evaluation: Developed a FastAPI testing harness benchmarking Llama 3.2 (3B), Phi-3 Mini (3.8B), and Mistral (7B) fully on-device via Ollama.
- Inference speed profiling: Measured raw performance where Phi-3 Mini led at 22.70 tokens/sec (323.99ms TTFT), followed closely by Llama 3.2 at 22.24 tokens/sec (427.29ms TTFT).
- Pydantic schema enforcement: Structured LLM outputs using validation schemas. Llama 3.2 achieved 100% compliance via retry reprompts, while Mistral 7B achieved 90% compliance zero-shot.
- Resource allocation tracking: Measured memory-bound constraints on Apple Silicon Mac mini (16GB RAM) where CPU load remained low (13–15%) but loaded memory hit 88.8% to 94.4% of RAM.
Generic Database MCP Server
- Zero hardcoding — connects to any DuckDB file and auto-discovers every table and column at runtime
- Type-aware quality checks: numeric columns get distribution stats + Z-score; VARCHAR gets cardinality; TIMESTAMP gets gap detection
- Ollama ReAct loop (llama3.2) iteratively calls MCP tools, drills into anomalies, and writes a plain-English RCA report
- FastAPI REST layer exposes drag-and-drop file upload, per-table quality checks, and RCA generation as HTTP endpoints
- Next.js dashboard visualises schema, null rates, distribution cards, and cardinality in a 3-step upload → inspect → report flow
AI-Powered Resume Parser
- PDF → structured JSON pipeline: pdfplumber extracts text → Llama 3.3 70B parses via Fireworks AI
- JSON schema enforcement: instructor library constrains LLM output to an exact Pydantic v2 model
- Retry mechanism: catches invalid outputs, re-prompts the LLM once, then fails gracefully — no silent errors
- Split-view UI: original PDF alongside experience timeline, color-coded skill tags, and education cards
- One-click JSON export, dark/light mode, drag-and-drop upload with animated progress steps
Production-Grade RAG Evaluation Pipeline
- Hybrid retrieval — BM25 sparse + contextual dense search — fed through a Cohere reranker
- Citation enforcement grounds every answer in source documents; no hallucinated references
- Prompts version-controlled in a config file — every change is tracked and reproducible
- Offline RAGAS script measures faithfulness, answer relevancy, and context precision
- GitHub Actions gate runs eval on every PR; merge blocked if any metric drops below threshold
Technical Skills
Generative AI
LLM Frameworks & APIs
MLOps
Cloud (AWS)
Core ML & Data
Agentic Coding Tools
15+
Years Experience
TCS → Shell → BT
$10M+
Business Value Generated
Across AI & ML initiatives
100K+
Documents Processed
Multimodal, production scale
7+
AI Systems Shipped
In telecom, energy & IT
Education
2023 – 2025
Liverpool John Moores UniversityM.S. Machine Learning & Artificial Intelligence
Liverpool, UK
2022 – 2023
IIIT BangaloreExecutive PG in Data Science & AI
Bangalore, India
Statistics & Probability · ML · NLP · Neural Networks · MLOps
2006 – 2010
New Horizon College of EngineeringB.E. Electronics & Communication
Bangalore, India
Visvesvaraya Technological University
Let's Connect
Open to senior data science and AI engineering roles. If you're building something ambitious with LLMs or agentic systems, I'd love to talk.