Gen AI Lead

Role Overview

You will lead the design development and deployment of enterprisegrade GenAI systems that go beyond traditional LLM pipelines. This role demands handson expertise with agentic frameworks orchestration runtimes multimodal models and scalable cloud-native architectures. You will guide a cross-functional team to build production-ready AI solutions that deliver measurable business impact.

Key Responsibilities

• Architect and lead endtoend GenAI solutions including agentic workflows autonomous decision loops retrieval systems and multimodel orchestration.
• Design scalable AI pipelines using modern frameworks such as LangGraph LangChain AgentCore AWS Bedrock and other emerging technologies (not limited to these).
• Build and optimize agents capable of tool use memory retries humanintheloop checkpoints and long-running workflows.
• Drive model strategy across open-source proprietary and cloud foundation models (Llama Claude GPT Mistral etc.).
• Lead cross-functional teams including data engineers ML engineers product managers and business stakeholders.
• Establish best practices for prompt engineering evaluation safety governance and observability.
• Integrate GenAI systems with enterprise platforms (ServiceNow Salesforce internal APIs data lakes microservices).
• Mentor and upskill teams on agentic design patterns RAG architectures and production deployment.
• Stay ahead of the curve by evaluating new frameworks runtimes and model capabilities to continuously evolve the AI roadmap.

Required Skills & Experience

• 7-12 years total experience with 3 years in leading GenAI/LLM initiatives.
• Strong hands-on experience with agentic frameworks (LangGraph AgentCore CrewAI AutoGen or equivalents).
• Deep understanding of LLM orchestration state management memory and multi-step reasoning.
• Expertise in RAG vector databases (Pinecone Weaviate FAISS) embeddings and retrieval optimization.
• Proficiency with cloud AI platforms (AWS Bedrock Azure OpenAI GCP Vertex AI).
• Solid programming skills in Python including async workflows API integrations and microservices.
• Experience deploying GenAI systems using Docker Kubernetes CI/CD and observability tools.
• Strong understanding of AI safety governance evaluation metrics and responsible AI principles.
• Ability to translate business problems into scalable AI architectures.

Preferred Skills

• Experience with multimodal models (vision speech structured data).
• Knowledge of MLOps and model lifecycle management.
• Exposure to enterprise automation platforms (ServiceNow UiPath Zapier Make).
• Prior experience building AI agents for recruitment HR operations or customer support is a plus.