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 productive 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.

📌 MLOps Engineer - Model Deployment & CI/CD (India)
🏢 Hexacorp
📍 India