MLOps Engineer

About The Role The role owns the reliability, scalability, and performance of machine learning infrastructure powering production AI systems at scale. The team works closely with machine learning engineers and data scientists to bridge the gap between experimental notebooks and robust, distributed production pipelines. Key Responsibilities Design, build, and maintain scalable MLOps infrastructure for training, fine-tuning, and serving large-scale machine learning models Implement robust CI/CD pipelines for ML workflows using tools like GitHub Actions, Argo Workflows, or Kubeflow Manage and optimize model serving endpoints using Kubernetes, Triton Inference Server, or vLLM to ensure low latency and high throughput Set up comprehensive monitoring and observability for deployed models to track GPU utilization, latency, and model drift Automate data ingestion, feature stores, and validation pipelines to ensure high data quality across training and inference environments Collaborate with security and platform engineering teams to ensure secure, compliant, and cost-effective cloud resource usage What We Are Looking For 3–6 years of experience in MLOps, DevOps, or machine learning engineering with a strong focus on infrastructure automation Deep expertise in Kubernetes, Docker, and container orchestration in cloud environments (AWS, GCP, or Azure) Hands-on experience with ML tooling such as Kubeflow, MLflow, Airflow, or Feast Strong proficiency in Python and Infrastructure as Code tools like Terraform or CloudFormation Solid understanding of distributed systems, networking, and GPU optimization for deep learning workloads Bonus: Experience deploying and serving large language models (LLMs) and generative AI applications in production