Collaborate with ML engineers and data scientists to design scalable ML pipelines and APIs.
Build latency-focused microservices and ensure reliability and performance.
Drive engineering excellence with code reviews, tests, and documentation.
Stay current with ML tooling and cloud infrastructure; improve team practices.
Monitor and optimize model performance and production infrastructure.
Partner with cross-functional stakeholders to deliver high-impact ML-powered products.

🎯 Requirements

Bachelor's degree in CS/Engineering or related field (or equivalent).
2–5 years of experience building and deploying ML systems in production.
Python, Java, PySpark.
scikit-learn, TensorFlow, PyTorch.
AWS: SageMaker, DynamoDB, Athena, Glue.
MLOps: CI/CD, Git, testing, MLflow; deployment with Airflow.

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