MLOps + ML Engineer

Role Overview:

Key Responsibilities:

• Architect and implement robust MLOps pipelines for model development, deployment, monitoring, and governance.
• Lead the operationalization of ML models using AWS SageMaker and other AWS services.
• Build and maintain CI/CD pipelines for ML workflows using tools like GitHub Actions, Jenkins, or AWS CodePipeline.
• Automate model lifecycle management including retraining, versioning, and rollback.
• Collaborate with data scientists, ML engineers, and DevOps teams to ensure seamless integration and scalability.
• Monitor production models for performance, drift, and reliability.
• Establish best practices for reproducibility, security, and compliance in ML systems.

Required Skills:

• 10+ years of experience in ML Engineering, MLOps, or related fields.
• Deep hands-on experience with AWS SageMaker, Lambda, S3, CloudWatch, and related AWS services.
• Strong programming skills in Python and experience with Docker, Kubernetes, and Terraform.
• Expertise in CI/CD tools and infrastructure-as-code.
• Familiarity with model monitoring tools (e.g., Evidently, Prometheus, Grafana).
• Solid understanding of ML algorithms, data pipelines, and production-grade systems.

Preferred Qualifications:

• AWS Certified Machine Learning Specialty or DevOps Engineer certification.
• Experience with feature stores, model registries, and real-time inference systems.
• Leadership experience in cross-functional ML/AI teams.

Primary Skills: