MLOps Engineer

About The Role The role is responsible for bridging the gap between machine learning research and robust, production-grade systems. This position owns the infrastructure, pipelines, and CI/CD workflows required to deploy, monitor, and scale machine learning models across the organization. Working alongside data scientists and platform engineers, the role ensures that model training is reproducible, deployments are automated, and production inference meets strict latency and reliability SLAs. Key Responsibilities 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) to ensure consistent feature engineering across offline training and online serving Deploy machine learning models as high-throughput, low-latency microservices using Triton Inference Server, KServe, or FastAPI Implement comprehensive monitoring and alerting systems for model drift, data quality, and system performance using Prometheus, Grafana, and Evidently AI Containerize ML workloads using Docker and orchestrate them on Kubernetes clusters across multi-tenant environments Establish CI/CD pipelines for automated testing, integration, and deployment of ML models (GitOps, GitHub Actions, GitLab CI) What We Are Looking For 3-6 years of experience in DevOps, MLOps, or Software Engineering with a strong focus on machine learning infrastructure Proficiency in Python and familiarity with shell scripting, infrastructure-as-code (Terraform), and cloud platforms (AWS, GCP, or Azure) Hands-on experience orchestrating workloads with Kubernetes and managing containerized applications at scale Familiarity with ML lifecycle platforms such as MLflow, Weights & Biases, or SageMaker Pipelines Solid understanding of software engineering best practices, including version control, automated testing, and code review Bonus: Experience with large language model deployment (vLLM, Ollama), distributed training frameworks (Ray, Spark), or a BS/MS in Computer Science or a related technical field Show more Show less