Experience: 8+ years in ML/AI (incl. DL/RL in production)

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 solid 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.

📌 Lead Data Scientist + Ai Bengaluru
🏢 Elgebra
📍 Bengaluru