Senior Machine Learning Engineer
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The role
Lead the design, development, and deployment of end-to-end machine learning solutions, including scalable training, inference, and evaluation pipelines with reliability, reproducibility, and observability.
Collaborate with data scientists, software engineers, product managers, and stakeholders to drive high-impact ML projects and foster a culture of scientific inquiry and continuous learning.
Identify and integrate generative AI and LLM-based components where they can meaningfully enhance ML systems.
Maintain operational excellence by codifying best practices and standards for monitoring, evaluation, retraining, and quality assurance across the ML lifecycle.
Leverage a broad toolset (Python, AWS, Databricks, Docker, Kubernetes, FastAPI, Terraform, Snowflake, GitHub) to build and deploy scalable ML systems for production use, including prediction, recommendation, personalization, and fraud detection.
Ensure ongoing reliability and maintenance through robust testing, experimentation, and observability, with clear communication to stakeholders.
Collaborate with data scientists, software engineers, product managers, and stakeholders to drive high-impact ML projects and foster a culture of scientific inquiry and continuous learning.
Identify and integrate generative AI and LLM-based components where they can meaningfully enhance ML systems.
Maintain operational excellence by codifying best practices and standards for monitoring, evaluation, retraining, and quality assurance across the ML lifecycle.
Leverage a broad toolset (Python, AWS, Databricks, Docker, Kubernetes, FastAPI, Terraform, Snowflake, GitHub) to build and deploy scalable ML systems for production use, including prediction, recommendation, personalization, and fraud detection.
Ensure ongoing reliability and maintenance through robust testing, experimentation, and observability, with clear communication to stakeholders.
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