Himanchal singh

Senior Applied data scientist @ dunnhumby

About

I am a Senior AI/ML Engineer focused on building and deploying production-grade ML and Generative AI systems. My work spans end-to-end ML pipelines, from data ingestion and model training to scalable deployment and monitoring. I specialize in LLM-based systems, including Retrieval-Augmented Generation (RAG), vector search, and agentic workflows over structured and unstructured data. I have designed systems involving document ingestion, chunking, embedding pipelines, vector databases, and LLM orchestration using modern GenAI frameworks. From an engineering perspective, I work extensively with Python, PyTorch, TensorFlow, and MLOps pipelines on cloud platforms, focusing on reliability, latency, and scalability. I have hands-on experience deploying models using Docker, Kubernetes, CI/CD (GitHub Actions), and cloud-native services. My GitHub showcases practical implementations of GenAI, NLP, and ML systems, including LLM-based applications, RAG pipelines, and experimentation with vector databases and model optimization. I am interested in engineering-heavy roles where I can design, build, and scale GenAI and applied ML systems in real-world production environments.

Country

India

City

Gurugram

Industry

Telecommunications

Skill

Docker, Fast api, GEN AI , ML Ops, Natural Language Processing (NLP), Rag, Large Language Models (LLM), Lang chain, Lang graph, Open ai, Semantic Search, VectorDB, Model Context Protocol (MCP), Tool calling, Functional calling, Prompt Engg, Hypothesis Testing, Google Cloud Platform (GCP), Machine Learning, Deep Learning

Experience

Senior Applied data scientist

dunnhumby

2025-8 - Present · 1 yr 2 mos

Gurugram, Haryana, India

Built a media-independent retail performance index translating consumer sentiment and behavior into actionable benchmarks. The index acted as a commercial entry point, generating retailer engagement and conversion into analytics-led advisory work.

AI/ML Computational Science

Accenture

2024-1 - 2025-8 · 1 yr 8 mos

Built and shipped production-grade AI/ML and Generative AI systems at enterprise scale, owning end-to-end architecture, deployment, and performance. Designed and deployed LLM-based systems (RAG, agentic workflows) powering low-latency, high-accuracy responses over large-scale enterprise data. Led development of a GenAI call analytics platform processing 200K+ calls/month, automating QA and insight generation and delivering $10M+ annual cost savings. Built a retrieval-augmented customer support system, cutting query resolution time by ~80% and reducing human agent dependency by ~40%. Drove critical engineering trade-offs across model accuracy, latency, cost efficiency, reliability, and cloud scalability.

Data Scientist

Accenture

2022-8 - 2024-2 · 1 yr 7 mos

Bengaluru, Karnataka, India

Built an end-to-end sales forecasting system for a US retailer, integrating internal and external signals and handling seasonality and data drift, improving accuracy by ~20%. Delivered a real-time conversion prediction system, addressing class imbalance and sparse signals, driving a 9× lift in marketing conversion. Engineered large-scale data pipelines to produce user personas, behavioral features, and segmentation datasets for downstream analytics and ML.