Job Title : MlOps engineer

Job Description :

This is an exciting opportunity to be part of a forward-thinking team where you'll get hands-on with the latest in MLOps tooling, work alongside data scientists and engineers, and play a key role in building scalable ML systems.

- Build ML Pipelines : Design and manage automated pipelines for data ingestion, training, testing, and deployment.

- Model Deployment : Use Docker and Kubernetes to deploy ML models reliably and scalably.

- CI/CD : Set up and maintain CI/CD pipelines using tools like Jenkins or GitLab CI.

- Orchestrate Workflows : Use Apache Airflow (or similar) to schedule and monitor your ML workflows.

- Monitor Performance : Implement monitoring tools (e.g., ELK Stack) to keep an eye on model performance, data drift, and system health.

- Collaborate Across Teams : Work closely with data scientists, backend engineers, and DevOps to ensure a seamless model lifecycle.

- Database Integration : Use relational (PostgreSQL) and NoSQL (MongoDB) databases within your ML workflows.

- Education : Bachelor's degree in Computer Science, Engineering, or a related field or equivalent hands-on experience.

- Experience : 2-3 year of experience in an MLOps or related role.

- Containerization Pro : You know your way around Docker and Kubernetes.

- Cloud Savvy : Youve worked with AWS, Azure, or GCP.

- Coding : Solid Python skills and familiarity with ML libraries like TensorFlow or PyTorch.

- Infrastructure as Code : Experience with Terraform or CloudFormation is a big plus.

- Monitoring Know-How : Familiarity with ELK Stack or similar tools for observability.

- Workflow Automation : You've worked with Apache Airflow or similar tools.

- Database Familiarity : Comfortable working with both SQL and NoSQL databases.