MLOps/DevOps/CloudOps

• Design/deploy ML pipelines and LLM-powered agents (RAG, Agentic AI) using AWS Bedrock, SageMaker, LangGraph
• • Develop, train, and productionize ML models (churn, recommendation, forecasting) with MLOps practices
• • Build RAG architectures, integrate LLM APIs, prompt engineering, and responsible AI guardrails
• • Strong Python (Scikit-learn, TensorFlow, PyTorch); hands-on SageMaker; vector DBs (FAISS, Pinecone, OpenSearch)
• • Must-have: Python, LangGraph/LangChain, Bedrock/OpenAI integration | Nice-to-have: LLM fine-tuning, LangSmith/W&B, AWS ML Specialty

Roles & Responsibilities

• Design, build, and maintain scalable MLOps and DevOps solutions on AWS.
• Develop and manage CI/CD pipelines for ML model deployment and application releases.
• Automate infrastructure provisioning using Infrastructure as Code (IaC) tools.
• Deploy, monitor, and optimize machine learning models in production environments.
• Manage containerized applications using Docker and Kubernetes.
• Implement model versioning, experiment tracking, and monitoring frameworks.
• Ensure security, reliability, and high availability of cloud infrastructure.
• Collaborate with Data Scientists, ML Engineers, and Development teams to operationalize ML workflows.
• Troubleshoot deployment, performance, and infrastructure-related issues.

Technical Requirements

• Strong experience with AWS Cloud Services (EC2, S3, Lambda, IAM, EKS, ECS, CloudWatch, SageMaker).
• Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI.
• Experience in DevOps tools: Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps.
• Proficiency in Python scripting and automation.
• Expertise in Docker and Kubernetes.
• Knowledge of Infrastructure as Code tools such as Terraform or CloudFormation.
• Experience with monitoring tools like CloudWatch, Prometheus, and Grafana.
• Strong understanding of Linux systems and networking concepts.
• Experience with Git version control.