Gen AI Lead

Role Overview

You will lead the design, development, and deployment of enterprise‑grade GenAI systems that go beyond traditional LLM pipelines. This role demands hands‑on 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 end‑to‑end GenAI solutions including agentic workflows, autonomous decision loops, retrieval systems, and multi‑model 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, human‑in‑the‑loop 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.