MLOps Engineer

\Role & responsibilities

1. Model Deployment & CI/CD

• Build and maintain CI/CD pipelines for ML model packaging, testing, and deployment across dev, test, and production environments.
• Support containerization and orchestration of model services using standard platform tooling.
• Implement controlled release patterns (staged rollouts, rollback procedures) for model updates.
• Contribute to reusable deployment templates and pipeline patterns that reduce rework across model teams.

2. Monitoring & Observability

• Implement monitoring for model performance, data drift, and pipeline health in production.
• Set up alerting and dashboards to flag degraded model accuracy, latency issues, or job failures.
• Support root-cause investigation of production incidents and contribute to post-incident fixes. Job Title: ML Ops Engineer Hiring
• Maintain logging and traceability so model behavior can be audited and reproduced.

3. Pipeline & Infrastructure Support

• Operate and maintain training, retraining, and batch-scoring pipelines on schedule.
• Manage model registry entries, versioning, and artifact lineage for deployed models.
• Support environment hygiene, including dependency management and base image updates.
• Partner with platform teams to ensure efficient use of compute resources for training and inference.

4. Collaboration & Enablement

• Work with Data Scientists and ML Engineers to translate model requirements into deployable services.
• Partner with Data Engineering to ensure consistent, reliable data feeds into ML pipelines.
• Document deployment patterns, runbooks, and operational standards to support team self-service.
• Communicate clearly on deployment status, risks, and dependencies to stakeholders.

Preferred candidate profile

• 4 to 7 years of hands-on experience in MLOps, ML engineering, or DevOps roles with exposure to machine learning workflows.
• Working knowledge of CI/CD tooling and practices applied to model deployment.
• Experience with containerization (Docker) and orchestration concepts (Kubernetes or equivalent).
• Proficiency in Python and SQL, with the ability to script and automate operational tasks.
• Familiarity with cloud platforms (Azure preferred) and their ML services.