ML Engineer AI COE (Secunderabad)

ML Engineer AI COE Hybrid Model Location - Hyderabad - Apply ML/AI solutions with awareness of business needs, system constraints, and business context - Build and own ML/DL models across complex data types geometries, part metadata, transactional data, and free-text notes - Contribute NLP and document understanding pipelines for technical drawings and unstructured manufacturing specs; build reusable components on our AWS Bedrock-based AI Platform - Tackle different complex ML problems: identify data issues, navigate model choices, and design transparent experiments - Own smallmedium ML/DL subsystems and features end-to-end - Work independently on assigned ML tasks while collaborating across teams - Suggest improvements at the feature level; explore and evaluate new techniques with guidance - Mentor junior peers to grow into ML; provide solid code, testing, and reviews - Contribute to feature-level design discussions and surface technical suggestions and improvements Must-Have Requirements - Valuable grounding in the mathematical foundations of ML and a relevant degree (computer science, simulation science, or equivalent) - 35 years of hands-on experience designing, training, and deploying complex ML/DL models in production using state-of-the-art frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost) - Proven ability to build models that serve as autonomous decision systems applied to critical business problems with large, heterogeneous data - Strong Python engineering fundamentals: clean, modular, testable code; data pipelines; model versioning; CI/CD for ML; packaging for production (e.g. Docker, service wrappers) - Solid ML experimentation practice: experiment tracking from scratch (e.g. W&B;, MLflow), model evaluation, and iterative improvement tied to measurable business outcomes - Solid understanding of data transformation techniques, languages, and libraries (e.g. Pandas, Polars, SQL, dbt) - Ability to work independently: break down larger problem definitions into concrete tasks, navigate model choices, and deliver production-ready ML implementations end-to-end .