Requirements

• Proven track record architecting and delivering production ML systems at scale in enterprise environments
• Deep expertise with AWS SageMaker (training, processing, pipelines, endpoints, registry) and complementary AWS services
• Expert-level Python and ML Model frameworks (e.g. PyTorch, TensorFlow, XGBoost)
• Strong thought leadership in MLOps automation, CI/CD for ML, and model lifecycle management
• Advanced experience designing explainability systems, reason codes, and governance artefacts
• Expertise in low‑latency inference architectures and real-time model serving
• Strong grounding in drift detection, telemetry pipelines, observability patterns, and model QA
• Experience shaping ML security practices, including cross‑account IAM, data minimisation, and PII-safe design
• Ability to influence architecture, mentor senior engineers, and set long‑term technical direction
• (Desirable) Exp...