Key Responsibilities :

- Assist in the design and development of MLOps pipelines for deployment and integration of ML models.

- Support data scientists and senior engineers in operationalizing machine learning models.

- Automate model training, testing, and deployment workflows using Python and shell scripting.

- Monitor models in production and flag performance issues or anomalies for resolution.

- Implement and maintain version control practices for ML models and data.

- Containerize ML services and applications using Docker.

- Set up and maintain Jenkins pipelines for continuous integration and delivery.

- Support Kubernetes-based deployment of ML workloads.

- Ensure adherence to security, data privacy, and governance standards.

- Document MLOps processes, workflows, and configurations for team knowledge sharing.

- Stay current with emerging MLOps tools, frameworks, and best practices.

- Participate in sprint planning, standups, and retrospectives in an Agile environment.

Skills :

- Hands-on experience or strong familiarity with MLFlow.

- Proficiency in Python; experience with automation and Shell/Bash scripting.

- Working knowledge of Docker for containerization.

- Experience with Jenkins or similar CI/CD tools.

- Basic understanding of Kubernetes for container orchestration.

- Understanding of machine learning concepts and the model development lifecycle.

- Familiarity with SDLC practices and Agile methodologies.

- Good analytical and problem-solving skills.

- Strong communication skills and ability to work collaboratively in a team.

- Bachelor's degree in Computer Science, Data Science, Information Technology, or a related field.