MLOps Engineer

Key Responsibilities:

Pipeline Development:

• Design, build, and maintain scalable and reliable MLOps pipelines for automating the machine learning lifecycle.

Model Deployment:

• Deploy machine learning models into production environments using Docker and Kubernetes for robust containerized infrastructure.

CI/CD Automation:

• Automate continuous integration and deployment (CI/CD) processes for ML workflows using Jenkins or similar tools.

Monitoring & Troubleshooting:

• Monitor deployed models and pipelines using observability tools like Prometheus and Grafana; proactively identify and resolve issues.

Collaboration:

• Work closely with data scientists, software engineers, and DevOps teams to ensure seamless model integration, reproducibility, and performance.

Governance & Best Practices:

• Ensure adherence to MLOps best practices including model versioning, governance, and auditability.

Infrastructure Optimization:

• Continuously optimize and scale MLOps infrastructure based on evolving business and technical requirements.

Required Skills & Qualifications:

• 3+ years of hands-on experience in MLOps or production-level machine learning deployment
• Strong expertise in Kubernetes and container orchestration
• Proficiency in building and deploying with Docker
• Experience setting up and managing CI/CD pipelines using Jenkins
• Familiarity with monitoring tools such as Prometheus, Grafana, or equivalent
• Solid scripting or coding experience with Python, Bash, or similar languages
• Exposure to ML lifecycle tools like MLflow, TFX, SageMaker, or similar (preferred)
• Strong communication and teamwork skills, with the ability to work across functional teams