MLOps Engineer

As an MLOps Engineer, you'll design, deploy, and maintain production-grade ML workflows across AWS and Azure using container orchestration, IaC, and CI/CD pipelines. You'll bridge DevOps and ML teams to automate model training, deployment, monitoring, and edge API services—ensuring reliable, scalable AI solutions.

Responsibilities

Architect and implement MLOps pipelines for ML model training, versioning, deployment, and monitoring using tools like MLflow, Kubeflow, SageMaker Pipelines, or Azure ML; Build and manage Infrastructure as Code (IaC) with Terraform for multi-cloud environments, including AWS EKS/ECS/Lambda and Azure AKS/Azure Functions; Design containerized applications with Docker and orchestrate them on Kubernetes (EKS/AKS) for high-availability ML inference and edge services; Develop CI/CD pipelines using Azure DevOps (ADO), GitHub Actions, AWS CodePipeline, or Azure Pipelines to automate deployments of Python/FastAPI microservices and Node.js backends; Create and optimize edge API applications (e.g., FastAPI-based services) for low-latency inference on AWS Lambda@Edge, Azure Functions, or ECS Fargate; Implement observability with Prometheus, Grafana, CloudWatch, Azure Monitor, and alerting for ML model drift, performance, and infrastructure health; Collaborate with data scientists and DevOps teams to productionize AI solutions, troubleshoot issues, and scale for high workloads; Write clean, production-ready code in Python, Node.js, and Bash for automation scripts, ETL processes, and API gateways.

Requirements

Bachelor's degree in Computer Science, Engineering, or related field; 4+ years of experience in DevOps/MLOps roles, with proven deployments on AWS and Azure; Expertise in: Cloud: AWS (EKS, ECS, Lambda, SageMaker, ECR) and Azure (AKS, Azure ML, Functions); IaC & Orchestration: Terraform, Docker, Kubernetes (EKS/AKS); Pipelines: Azure DevOps (ADO), Jenkins, GitLab CI, or AWS/Azure-native tools; Programming: Python (FastAPI, Pandas, Scikit-learn), Node.js; ML Ops: Model deployment, versioning, monitoring (e.g., Seldon, KServe); Hands-on experience building edge services and API applications for real-time inference; Strong problem-solving skills in multi-cloud environments.

Nice to have

Certifications: AWS Certified Machine Learning – Specialty, Azure AI Engineer Associate, CKA/CKAD, Terraform Associate; Experience with vector databases (Pinecone, FAISS), serverless ML, or GenAI fine-tuning; Knowledge of React.js for dashboarding ML metrics.