LLM Agentic AI Lead

Job Description

We are seeking an LLM Agentic AI Lead to design, build, and deploy AI agents that solve business problems. This person will lead the technical direction for agentic AI solutions, working with stakeholders to identify use cases and guide a team from prototype through production.

The ideal candidate has hands-on experience with large language models, retrieval augmented generation (RAG), tool calling, and agent orchestration. They can design reliable workflows that connect LLMs to enterprise data and APIs, and explain technical decisions clearly to business and engineering teams.

Key Responsibilities

• Lead the architecture and development of agentic AI solutions using LLMs, RAG, tools, and APIs.
• Design agent workflows that can retrieve information, reason through tasks, take appropriate actions, and return useful results.
• Guide decisions on model selection, prompting, embeddings, vector databases, and retrieval pipelines.
• Establish approaches for evaluating agent performance, including response quality, accuracy, latency, and cost.
• Build safeguards such as access controls, human review for sensitive actions, and monitoring for production agents.
• Partner with stakeholders to turn business needs into practical AI use cases and delivery plans.
• Mentor engineers and communicate solution designs through diagrams and technical documentation.

Required Skills

• 3+ years of experience with Python, machine learning, and AWS.
• Hands-on experience developing LLM applications and RAG solutions.
• Experience building agentic workflows with frameworks such as LangChain, LangGraph, or similar tools.
• Experience integrating LLMs with APIs, tools, enterprise systems, and data sources.
• Familiarity with AWS Bedrock models and prompting strategies.
• Knowledge of embeddings, vector databases, and information retrieval.
• Experience with prompt engineering, LLM evaluation, and production monitoring.
• Ability to lead technical design discussions and explain solutions to varied audiences.

Preferred Skills

• Experience with PyTorch, TensorFlow, or Hugging Face Transformers.
• Experience fine-tuning models for specific applications.
• Familiarity with CI/CD, Docker, Kubernetes, and orchestration tools such as ArgoCD.
• Experience deploying user interfaces for AI applications.