AI Engineer + Data Scientist - Lead _Bangalore
Summary
Lead AI Engineer/Data Scientist role in Bengaluru (hybrid, onsite mandates) owning end-to-end enterprise AI solutions: architecting deep learning and reinforcement learning systems, optimizing production inference with TensorRT/ONNX/Triton, scaling training on Azure ML and AKS, and mentoring engineers while setting team-wide technical standards.
- Skills- AI Engineer + Data Scientist - Lead
- Work Mode (Onsite / Hybrid / Remote) Hybrid
- Work Location - Bangalore
- Years of experience required for this role 8+ Years
Mandates :F2F for final round of interview.
AI Technical Lead
Experience: 8+ years in ML/AI (incl. DL/RL in production)
Reports To: Manager, AIML
Education: Master's (preferred) or Bachelor's in CS, Data Science, or Mathematics
About the Role
Own the full lifecycle of enterprise-scale AI solutions architecture through production — and set technical best practices, governance, and standards across the team. A player-coach leadership role.
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); balance accuracy vs. latency.
• Scale training/inference on Azure ML, AKS/ARO, and distributed infrastructure.
• Lead technical solutioning, manage stakeholders, and mentor engineers.
Must-Have Skills
• 7+ years 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/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.