Staff MLOps Engineer

Technical Leadership & Collaboration

Mandatory Skills Required

• 11–13 years of overall experience, with 5+ years in MLOps , ML Platform, or ML Infrastructure roles.
• Strong experience deploying and operating machine learning models in production on AWS.
• Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch , Scikit-learn.
• Deep hands-on experience with Docker and Kubernetes (EKS).
• Strong understanding of Amazon SageMaker and its ecosystem.
• Experience with CI/CD systems and Git-based workflows.
• Solid background in distributed systems, system design, and cloud architecture.

Preferred / Nice-to-Have Skills

• Experience with SageMaker Feature Store, Pipelines, Model Registry, or MLflow .
• Exposure to LLMOps / GenAI on AWS (Bedrock, custom LLM deployment, vector databases like OpenSearch, Pinecone).
• Experience with streaming and real-time pipelines (Kafka, Kinesis, Spark).
• Experience in regulated or high-scale environments (finance, healthcare, retail, etc.).
• AWS certifications (Solutions Architect, Machine Learning Specialty) are a plus.

Soft Skills

• Strong ownership and decision-making ability at a Staff level.
• Excellent communication skills across engineering, data science, and leadership teams.
• Ability to balance short-term delivery with long-term platform vision.
• Passion for building reliable, scalable, and maintainable ML systems.

Qualifications Required

• Bachelor’s degree (B.A.) from four-year college or university, or equivalent combination of education and experience.
• 11–13 years of overall experience, with 5+ years in MLOps , ML Platform, or ML Infrastructure roles.

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