Description

Architect and deliver Agentic AI systems, leading the design of end-to-end ML and GenAI architectures including data pipelines, vector databases, and RAG patterns. You will define solution patterns, oversee model development, and ensure scalable deployment using MLOps practices.

Responsibilities

• Architect LLM-powered systems and multi-agent orchestration frameworks using Langgraph, LlamaIndex, or Semantic Kernel.
• Design end-to-end GenAI architectures with guardrails, AI governance, and human-in-the-loop workflows.
• Build, fine-tune, and optimize ML/Deep Learning models using Python and TensorFlow.
• Architect scalable CI/CD pipelines and deploy AI systems using Docker and Kubernetes.
• Lead experimentation for autonomous agents, task decomposition, and self-improving AI workflows.

Required Skills

• 10-15 years overall experience with 8+ years in AI/ML Architecture.
• 3+ years of experience in Agentic AI and LLM-based systems.
• Strong proficiency in Python and mainstream ML frameworks (TensorFlow).
• Hands-on experience with Docker, Kubernetes, and AWS.
• Experience designing and deploying AI agents on enterprise-grade runtimes (e.g., Amazon Bedrock AgentCore).
• Knowledge of MLOps, CI/CD, and scalable deployment practices.
• Understanding of agent runtime capabilities including state management, memory handling, and asynchronous execution.

Preferred Skills

• Experience in the Pharmacy or Healthcare domain, including familiarity with FHIR, HL7, and HIPAA compliance.
• Experience with LLM-as-a-judge, hallucination detection, and bias/safety evaluation.

Key Skills

Education

Bachelor's degree