Lead Data scientist

Overview

Lead Data Scientist who architects, builds, and operates production-grade Machine Learning and Generative AI systems across the full lifecycle.

What you'll do

• Design, train, and optimize ML models for prediction, classification, ranking, forecasting, anomaly detection, NLP, and recommendations.
• Build enterprise LLM applications using RAG (retrieval-augmented generation), MCP, and fine-tuning, including tool use and agentic workflows.
• Deploy and monitor real-time and batch inference on Azure with CI/CD, rollout, rollback, observability, and drift/performance monitoring.
• Orchestrate end-to-end ML pipelines (train → evaluate → deploy), automating dataset/version management and retraining triggers.
• Own analytics outputs as products (dashboards, datasets, internal tools) using scalable SQL and Python transformations.
• Partner with senior stakeholders to translate data into decision-ready recommendations and success metrics.
• Implement responsible AI and security guardrails (prompt injection defenses, sensitive data protections, output validation, auditability).

What you'll need

• 7–12+ years in hands-on Data Science / ML Engineering with multiple production deployments owned end-to-end.
• Strong Python for production-quality coding and solid computer science fundamentals.
• Strong SQL for data access and validation.
• Depth in ML, including traditional ML and at least one deep learning framework (PyTorch or TensorFlow).
• GenAI implementation experience with RAG/MCP/fine-tuning, embeddings/vector search, prompt orchestration, and evaluation harnesses.
• Production deployment experience on AWS or Azure (model/LLM app deployment, API serving, scaling, monitoring).
• MLOps experience including experiment tracking, model registry, CI/CD, and pipeline orchestration (e.g., MLflow patterns).

Details

• Role location: Bangalore.
• Work arrangement: office role; in-person at least 3 days per week.