MLOps Engineer

The role owns the infrastructure, orchestration, and scaling pipelines that take machine learning models from local experiments to resilient, low-latency production services.

The team works closely with machine learning engineers and data scientists to build robust MLOps practices, automated CI/CD pipelines, and comprehensive monitoring systems.

Key Responsibilities

• Design, build, and maintain scalable MLOps infrastructure and pipelines using Kubernetes, Docker, and Terraform on cloud platforms
• Implement automated CI/CD pipelines for model training, testing, and deployment to ensure rapid and reliable releases
• Manage and optimize model serving infrastructure using Triton, TorchServe, or vLLM to achieve strict latency and throughput SLAs
• Set up end-to-end monitoring and observability systems for data drift, concept drift, and resource utilization using Prometheus and Grafana
• Collaborate with data engineers to enforce feature store integrity and ensure seamless parity between training and inference data
• Establish security, governance, and cost-optimization practices for all cloud-hosted machine learning workloads

• 3–7 years of experience in MLOps, DevOps, or machine learning engineering with a heavy focus on production infrastructure
• Strong proficiency in Kubernetes, containerization, and infrastructure-as-code tools like Terraform or CloudFormation
• Hands-on experience with modern model serving frameworks and building automated model CI/CD pipelines
• Solid understanding of cloud platforms such as AWS, GCP, or Azure, including IAM, networking, and cluster management
• Strong software engineering fundamentals in Python and Bash scripting for automation and tooling
• Bonus: Experience managing LLM infrastructure, vector databases, or high-throughput real-time inference systems at scale