MLOps Engineer

Role overview

Own the infrastructure, orchestration, and continuous deployment pipelines that move machine learning models from experimentation to production serving. Build and maintain systems that enable low latency, high availability, and robust monitoring for AI applications across cloud environments.

Responsibilities

• Design and implement scalable MLOps pipelines for automated model training, validation, and deployment using Kubernetes and Kubeflow.
• Build and maintain feature stores and data pipelines to ensure consistency between training and inference environments.
• Manage cloud infrastructure on AWS or GCP, optimizing resource allocation for GPU/TPU training and low-latency inference endpoints.
• Integrate automated monitoring to detect data drift, concept drift, and model performance degradation in production.
• Establish CI/CD pipelines for ML artifacts, model registries, and dataset/experiment version control.
• Collaborate with security and engineering teams to ensure compliance, data privacy, and secure model serving at scale.

Requirements

• 3–6 years experience in MLOps, DevOps, or machine learning engineering, with strong production-infrastructure focus.
• Hands-on experience with containerization and orchestration: Docker, Kubernetes, and Helm.
• Proficiency with cloud ML/infrastructure tooling: AWS SageMaker, GCP Vertex AI, or MLflow.
• Strong software engineering skills in Python and Bash.
• Experience building automated CI/CD workflows using GitHub Actions or GitLab CI.
• Solid understanding of networking, security, and performance tuning for distributed computing frameworks such as Spark or Ray.

Bonus

• Experience with LLM serving optimizations: vLLM, Triton Inference Server, or TGI in high-throughput environments.