Engineer/ML Ops Key Responsibilities 1. 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. 8.

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 1. Hands-on experience or strong familiarity with MLFlow.

- Proficiency in Python;

experience with automation and Shell/Bash scripting.

- Working knowledge of Docker for containerization. 4.

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.
- Positive 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.

📌 MLOPS Engineer to 5 years ( Immediate joiner) (Hyderabad)
🏢 Snapper Future Tech
📍 Hyderabad