Posted:None|
Platform:
Hybrid
Full Time
Lead the design, development, and deployment of machine learning models across various business domains.
Architect and implement end-to-end ML pipelines using AWS Sage maker AI, MLflow for experiment tracking, model versioning, and lifecycle management.
Oversee model training, tuning, and deployment using Amazon SageMaker AI, including Pipelines, Model Registry, and Model Monitor.
Mentor and guide a team of data scientists, ensuring best practices in coding, experimentation, and documentation.
Collaborate with engineering, product, and business teams to translate complex problems into data-driven solutions.
Establish and enforce MLOps standards for reproducibility, scalability, and monitoring of ML models in production.
Drive innovation by evaluating and integrating emerging tools and technologies in the ML ecosystem
Deep understanding of machine learning algorithms, feature engineering, and model evaluation techniques.
Experience with cloud platforms and containerization tools like Docker.
Familiarity with CI/CD pipelines, data versioning, and monitoring tools.
Experience with SageMaker Studio, SageMaker Pipelines, and SageMaker Clarify.
Knowledge of feature stores, data governance, and responsible AI practices.
Strong communication and leadership skills with the ability to influence across teams.
Experience in deploying ML solutions in regulated industries (e.g., finance, healthcare) is a plus.
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