Job Description:
- 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
Key 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
Preferred Skills:
Technology->AI-Data science->PYTHON,Technology->AI-AI Engineering->MLOps,Technology->Cloud Platform->Amazon Webservices DevOps->Amazon CodeDeploy
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