Data Scientist - AI/ML

Please read JD carefully before applying. You will only be shortlisted if your resume if aligned with the JD. We are looking for a Data Scientist who writes production code every day, ships models that receive real traffic, and is as comfortable debugging a RAG pipeline as tuning an XGBoost model for pricing signals. This is a hands-on individual contributor role. You report to the CTO and work in collaboration with the ML Engineer. The ideal candidate brings a strong classical ML foundation and has meaningfully expanded their stack into GenAI engineering : RAG, embeddings, LLM tool use, and prompt evaluation. You are not waiting to be assigned problems; you identify gaps in the platform, propose solutions, and ship them independently. We are not looking for someone focused on people management, strategy presentations, or org-building. We want someone who wants to go deep, stay technical, and own the full ML lifecycle end-to-end across a genuinely complex, high-stakes domain. What Youll Do : - Build and own production ML models for pricing signal detection, demand forecasting, lead time prediction, lifecycle risk scoring, and inventory age risk primarily using XGBoost, LightGBM, CatBoost, and time-series methods (ARIMA, Prophet, temporal Transformers where warranted). - Own the full ML lifecycle end-to-end : feature engineering, model training, offline evaluation, A/B testing, production deployment, drift monitoring, and scheduled retraining pipelines. - Make principled decisions on when a gradient-boosted model or a simple statistical heuristic outperforms a large LLM; optimising for cost, latency, and explainability at every layer. - Build SHAP-based interpretability outputs and business-readable model explanations so that pricing and risk models are trusted, auditable, and adoptable by product and operations teams. - Design and execute A/B tests and experimentation frameworks to measure the business impact of each model within its first sprint in production. - Build RAG pipelines for datasheet extraction, BOM parsing, RFQ drafting, and supplier communication; grounding LLMs in proprietary component intelligence data. - Design and implement embedding strategies: select embedding models, define chunking approaches, implement hybrid dense-sparse retrieval (Qdrant, Weaviate, pgvector), and build reranking layers tuned per use case. - Integrate frontier model APIs (OpenAI, Anthropic, Gemini) and self-hosted open-source models (Llama 3, Mistral, Qwen via HuggingFace) based on cost, latency, and accuracy benchmarks; with documented trade-off rationale. - Build and maintain prompt engineering systems that are version-controlled, systematically evaluated, and reproducible, not ad-hoc prompts left in notebooks. - Implement LLM evaluation pipelines using RAGAS or equivalent frameworks to track retrieval quality, answer relevance, and hallucination rates across all RAG systems. - Design, build, and maintain feature pipelines using dbt, Pandas, and Polars, transforming raw procurement and component data into high-quality training and serving features. - Work closely with the data stack (Postgres, Redshift, S3) to build efficient, reproducible feature stores and training datasets with clear lineage. - Implement data quality checks and validation layers for ML inputs, catching distribution shifts at the data layer before they surface in model behaviour. - Build and document feature registries so that features engineered for one model are discoverable and reusable across the platform. - Apply leakage-free cross-validation design : proper temporal splits, group k-fold, and nested CV strategies appropriate to each prediction task. - Own experiment tracking using MLflow (self-hosted), every experiment logged, every result reproducible, every model version documented with a clear performance baseline. - Containerise model inference services using Docker and deploy on AWS (SageMaker, Lambda, ECS) with clear latency SLAs, runbooks, and rollback procedures. - Implement production monitoring using Evidently AI or equivalent: data drift, model performance drift, and business KPI alignment, automated alerts before humans notice degradation. - Maintain clean CI/CD pipelines (GitHub Actions) for model deployment: code review, automated unit and integration tests, staging promotion, and production release gates. - Actively use AI-assisted development tools (Cursor, GitHub Copilot, Windsurf) to compound your own output velocity, vibe coding is a first-class practice here, not a side habit. - Build Python-based tools, function schemas, and data connectors that the Director uses in multi-agent orchestration workflows built on LangGraph, CrewAI, or AutoGen. - Design and expose MCP-compatible tool endpoints for model-serving, data retrieval, and scoring functions, making ML model outputs composable and consumable by AI agents across the platform. - Support agentic workflow testing : write evaluation suites Pl