Agentic AI Lead

Agentic AI Lead

Contract

Carlotte, NC

Responsibilities

• As the Onsite Agentic AI Lead, you will:
• Lead the hands-on development and deployment of Agentic AI solutions using frameworks such as LangGraph, AutoGen, and CrewAI.
• Utilize Python extensively for core language development, API integration, and advanced prompt design.
• Work with Large Language Models (LLMs), focusing on tool/function calling capabilities.
• Design and implement Retrieval Augmented Generation (RAG) pipelines and integrate with various knowledge bases.
• Develop and optimize agent workflows and reusable templates for efficient AI system creation.
• Implement and manage vector databases (e.g., FAISS, Chroma, Pinecone) for efficient data retrieval.
• Set up and maintain observability stacks, including logging and drift/bias monitoring, to ensure the health and performance of AI systems.
• Apply a deep understanding of the Agent Development Lifecycle (ADLC) from conception to deployment.
• Ensure governance and compliance for AI systems, addressing privacy, safety, and auditability.
• Integrate security and risk checklists into all AI deployments.
• Leverage expertise in cloud-native architecture, specifically AWS, for scalable and robust AI solutions.
• Liaise effectively with our India-based AI team, translating complex business requirements from executives and stakeholders into clear technical specifications. Cultivate strong relationships with internal stakeholders and identify new opportunities for AI integration and growth within the account.
• Present technical concepts and project updates clearly and concisely to executive leadership and non-technical audiences.

Qualifications

• 3-4 years of hands-on experience designing and deploying AI/LLM systems in production.
• 5-7 years of experience in AI/ML systems architecture.
• Proficiency in Agentic AI frameworks (LangGraph, AutoGen, CrewAI).
• Expertise in Python, including API integration and prompt engineering.
• Strong understanding of LLMs and tool/function calling.
• Demonstrated experience with RAG pipelines and knowledge base integration.
• Familiarity with vector databases (FAISS, Chroma, Pinecone, etc.).
• Experience with observability stack setup (logging, drift/bias monitoring).
• Solid knowledge of the Agent Development Lifecycle (ADLC).
• Understanding of governance, compliance, security, and risk in AI deployments.
• Experience with cloud-native architecture, particularly AWS.
• Exceptional executive communication and presentation skills.
• Proven ability in stakeholder management and relationship building.

Education

• Bachelor's or Master's degree in Mathematics, Statistics, Computer Science, Data Science, Artificial Intelligence, or a similar quantitative field is required.