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 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.