AWS DevOps/MLOps Engineer

AWS DevOps/MLOps Engineer

We are seeking an experienced AWS DevOps/MLOps Engineer to join our team and support cutting‑edge Agentic AI initiatives. In this role, you will design, build, and maintain scalable, automated machine learning infrastructure that powers autonomous AI agents. You will work closely with data scientists, ML engineers, and platform teams to deliver robust CI/CD pipelines, infrastructure as code, and monitoring solutions tailored for ML workloads.

Key Responsibilities

• Architect and implement AWS‑native MLOps pipelines using services such as SageMaker, ECS/EKS, Lambda, Step Functions, and CodePipeline.
• Develop Infrastructure as Code (IaC) with Terraform or AWS CDK to provision reproducible ML environments.
• Automate model training, evaluation, deployment, and monitoring workflows to enable rapid iteration.
• Build self‑service tooling for data scientists to experiment, version, and promote models with minimal friction.
• Implement observability (logging, metrics, tracing) for ML services using CloudWatch, Prometheus/Grafana, or Datadog.
• Collaborate on security and compliance best practices, including IAM least‑privilege, encryption, and audit logging.
• Drive cost optimization strategies for GPU/TPU compute and storage across the ML lifecycle.

Why Join Us?

You will be at the forefront of Agentic AI, shaping infrastructure that enables autonomous decision‑making systems. We offer a collaborative culture, continuous learning budget, and the opportunity to work with state‑of‑the‑art generative AI models in production.

This position is based in India with flexible remote options.

• 5+ years of professional DevOps/Cloud engineering experience, with at least 2 years focused on MLOps.
• Deep expertise in AWS (SageMaker, ECS/EKS, Lambda, IAM, VPC, CloudFormation/CDK/Terraform).
• Strong proficiency in Python and/or Go for automation and tooling.
• Hands‑on experience with container orchestration (Kubernetes, Docker) and CI/CD (GitHub Actions, GitLab CI, Jenkins, AWS CodePipeline).
• Familiarity with ML frameworks (PyTorch, TensorFlow, Hugging Face) and model serving (Triton, TorchServe, BentoML).
• Solid understanding of ML lifecycle: data versioning, experiment tracking, model registry, feature stores.
• Experience with monitoring/alerting stacks (Prometheus/Grafana, CloudWatch, Datadog) for ML workloads.
• Excellent communication skills and ability to collaborate across data science, engineering, and product teams.
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.