Lead- Data Scientist + AI Engineer

About the Role

Own the full lifecycle of enterprise-scale AI solutions, from architecture through production and set technical best practices, governance and standards across the team. A player-coach leadership role.

Experience: 7+ years in ML/AI (including DL/RL in production)

Location: Bangalore (Whitefield) - Work from office, 5 days a week

Notice Period: Immediate joiners preferred (0-15 days)

Interview Mode: In-person interviews (Whitefield, Bangalore)

Education: Master's preferred, or Bachelor's in CS, Data Science or Mathematics

Please note: An online assessment is mandatory as part of the selection process.

Key Responsibilities

• Architect end-to-end DL/RL and agentic AI solutions from design to production
• Set technical standards, governance and evaluation frameworks across the team
• Optimize models for production inference (TensorRT/ONNX/Triton), balancing accuracy and latency
• Scale training and inference on Azure ML, AKS/ARO and distributed infrastructure
• Lead technical solutioning, manage stakeholders and mentor engineers

Must-Have Skills

• 7+ years of ML/AI with production DL and/or RL systems
• Mastery of DL frameworks (PyTorch/TensorFlow/JAX) and strong applied math (linear algebra, probability, optimization)
• Deep learning across CNNs, transformers and sequence models; RL agents (PPO, SAC, TD3, CQL)
• Inference optimization (TensorRT/ONNX/Triton) and accuracy vs latency benchmarking
• Model serving and containerized deployment at scale (Docker/Kubernetes, AKS/ARO)
• Cloud-scale training and inference on Azure ML with distributed training and MLOps/CI-CD
• Ability to define standards, governance and evaluation frameworks across a team
• Proven technical leadership, mentoring and stakeholder communication
• Track record of 6-10 production deployments with measurable business impact

Nice to Have

• Agentic AI architecture (LangGraph, AutoGen, CrewAI) and RAG pipeline design
• RL libraries (Ray RLlib, Stable-Baselines3, Gymnasium); multi-agent RL and simulation (MuJoCo)
• Model compression/quantization and GPU-efficiency optimization
• Agentic platform evaluation (Azure AI Foundry, AWS Bedrock, Databricks AgentBricks)
• Optimization/OR background (LP/MIP, Gurobi/CPLEX); Julia/SciML exposure
• Semiconductor, manufacturing or supply-chain domain experience