What you will be doing (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.

- 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.

What you bring (Qualifications) :

Required :

- 4 - 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.

Preferred :

- Exposure to model registry, experiment tracking, or feature store tools (MLflow, Databricks, or equivalent).

- Experience with monitoring/observability tooling for data or ML workloads.

- Background in retail, consumer goods, or other data-intensive industries.

- Familiarity with Databricks and Delta Lake-based environments.