ML Ops Engineer

We are looking for a proactive and detail-oriented AI OPS Engineer to support the deployment, monitoring, and maintenance of AI/ML models in production. This role will focus on MLOps practices including model versioning, CI/CD, observability, and performance optimization in cloud and hybrid environments.

Role & responsibilities

• Build and manage CI/CD pipelines for ML models using platforms like MLflow, Kubeflow, or SageMaker.
• Monitor model performance and health using observability tools and dashboards.
• Ensure automated retraining, version control, rollback strategies, and audit logging for production models.
• Support deployment of LLMs, RAG pipelines, and agentic AI systems in scalable, containerized environments.
• Collaborate with AI Developers and Architects to ensure reliable and secure integration of models into enterprise systems.
• Troubleshoot runtime issues, latency, and accuracy drift in model predictions and APIs.
• Contribute to infrastructure automation using Terraform, Docker, Kubernetes, or similar technologies.

Preferred candidate profile

• Experience in DevOps, MLOps, or platform engineering roles with exposure to AI/ML workflows.
• Hands-on experience with deployment tools like Jenkins, Argo, GitHub Actions, or Azure DevOps.
• Strong scripting skills (Python, Bash) and familiarity with cloud environments (AWS, Azure, GCP).
• Understanding of containerization, service orchestration, and monitoring tools (Prometheus, Grafana, ELK).