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Machine Learning Engineer - Capgemini Government Solutions
ML Engineer
Machine Learning Engineer responsible for designing, building, and deploying ML models and data pipelines on cloud platforms, leveraging Python, deep‑learning frameworks, and container orchestration to support critical government applications.
About the role
Key Responsibilities
- Design, develop, and productionize machine‑learning models using Python and frameworks such as TensorFlow or PyTorch.
- Build scalable data pipelines and feature stores, integrating with SQL databases and cloud storage.
- Containerize applications with Docker and orchestrate deployments on Kubernetes or AWS services.
- Collaborate with data engineers and domain experts to translate business requirements into robust AI solutions.
- Implement monitoring, testing, and continuous‑integration/continuous‑deployment (CI/CD) practices to ensure model reliability and security.
Requirements
- Strong proficiency in Python and experience with deep‑learning libraries (TensorFlow, PyTorch, Scikit‑learn).
- Hands‑on experience deploying ML workloads on AWS (SageMaker, EC2, S3) and using container technologies (Docker, Kubernetes).
- Solid understanding of data engineering concepts, SQL, and building ETL pipelines.
- Demonstrated ability to write clean, production‑grade code and follow CI/CD best practices.
- Active security clearance or ability to obtain one.
Skills
pythontensorflowpytorchawsdockerkubernetessql