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 robust 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.

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