Job Description

This role aims to design, implement, and maintain scalable, secure, and reliable MLOps infrastructure and CI/CD pipelines to enable rapid and high-quality delivery of machine learning models and data-driven services to production. The role bridges ML/Development and Operations, driving automation, reliability, monitoring, and operational excellence across environments.

Key Responsibilities

• Build and operate end-to-end pipelines for training, validation, packaging, and deployment across dev/test/prod.
• Implement CI/CD for code, data, and model artifacts with quality gates, approvals, and rollbacks.
• Deploy and scale ML services using Docker and Kubernetes (real-time and batch), with safe rollout strategies.
• Set up model registry & experiment tracking and enforce reproducible, versioned releases (e.g., MLflow or equivalent).
• Implement monitoring/alerting for service health, latency, errors, resource usage, plus ML signals ( drift, data quality, model performance).
• Define operational standards (SLIs/SLOs, incident response, RCA, runbooks) and continuously improve reliability.
• Enforce security best practices (IAM/RBAC, secrets management, network controls, audit logging) and collaborate with DS/ML/Data teams.

Requirements

Requirements

• 3–7 years in MLOps/DevOps/Platform roles with production ML exposure.
• Strong CI/CD + automation, solid Python and Linux, strong troubleshooting.
• Hands-on with Docker + Kubernetes and observability tools (Prometheus/Grafana, ELK, OpenTelemetry or similar)