Description

You will design, develop, and optimize complex data pipelines using machine learning engineering best practices.

Responsibilities

• Implement agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for secure tool orchestration.
• Build LLM-based applications incorporating RAG, structured output, and evaluation frameworks.
• Deploy and manage models using AWS SageMaker pipelines, ECS/ECR, and Lambda; own CI/CD and monitoring.
• Integrate Okta/JWT token for API and service authentication and enforce token validation.
• Deliver artifacts required by MDLC/MPLC, including Model Documents and Data Dictionary.

Required Skills

• 6+ years of experience in AI/ML engineering, data engineering, and MLOps implementation.
• Proficiency in Python and experience with TensorFlow, PyTorch, and SciKit-learn.
• Experience building scalable data pipelines and managing model lifecycles in cloud environments.
• Hands-on experience with Generative AI, specifically building LLM applications with RAG.
• Familiarity with agentic frameworks like LangGraph and AutoGen.
• Experience with AWS ML Stack components (SageMaker, Glue, Bedrock, Data Pipelines, Lambda).
• Knowledge of containerization (Docker) and orchestration tools (Kubernetes).
• Solid grasp of software engineering principles, including Git and testing.

Key Skills

Education

Any Graduate