MLOps Engineer

The role bridges ML research and production-grade systems, owning infrastructure, pipelines, and CI/CD for deploying and scaling ML models.

• Design, build, and maintain robust ML pipelines using Kubeflow, Airflow, or Prefect for automated model retraining and batch inference
• Develop and manage feature stores (e.g., Feast or Tecton) for consistent feature engineering
• Deploy ML models as high-throughput, low-latency microservices using Triton Inference Server, KServe, or FastAPI
• Implement monitoring and alerting for model drift and system performance using Prometheus, Grafana, and Evidently AI
• Containerize ML workloads with Docker and orchestrate on Kubernetes
• Establish CI/CD pipelines for automated testing and deployment (GitOps, GitHub Actions, GitLab CI)

• 3-6 years of experience in DevOps, MLOps, or Software Engineering with focus on ML infrastructure
• Proficiency in Python and familiarity with shell scripting, Terraform, and cloud platforms (AWS, GCP, or Azure)
• Hands-on experience with Kubernetes and containerized applications at scale
• Familiarity with ML lifecycle platforms such as MLflow, Weights & Biases, or SageMaker Pipelines
• Solid understanding of version control, automated testing, and code review

• Experience with large language model deployment (vLLM, Ollama), distributed training frameworks (Ray, Spark), or a BS/MS in Computer Science or related field