Lead AI Engineer – Agentic LLM Pipelines & Tools

Lenovo; Chicago (IL); On-site; USD 200,000 - 250,000; Full time

Job summary

Lenovo is seeking a technical lead to own the full agentic stack—from LLM orchestration to production deployment. You will design and ship agentic pipelines, remember and reason over data, and integrate tools across multi-step tasks.

You will build evaluation frameworks for reliability and safety, maintain RAG systems and vector stores, and scale deployments in production. Collaboration with AI engineers, ML scientists, and product managers is essential.

Qualifications

• 6+ years of ML/AI industry experience with hands-on LLM experience.
• Bachelor's degree in CS, math, physics, or related field.
• Experience with NLP.
• Fluent in Python and modern ML stack (PyTorch, Hugging Face, vLLM or equivalent).
• Proven experience with agentic frameworks (LangGraph, AutoGen, CrewAI, or custom) and tool-use patterns.
• Solid grasp of RAG architecture, embeddings, and LLM evaluation.
• Strong software engineering fundamentals (Python plus another language).
• Experience with Git and CI/CD.
• Experience deploying ML models with Docker and Kubernetes.

Responsibilities

• Design and ship agentic LLM pipelines: multi-step reasoning, tool use, memory, and planning.
• Build evaluation frameworks to measure agent reliability and safety across tasks.
• Build and maintain RAG systems, vector stores, and external tool integrations.
• Deploy and scale agent systems in production; translate research into working systems.
• Be a technical lead, collaborating with AI engineers, ML scientists, and product managers.
• Keep abreast of AI/ML developments and apply cutting-edge methods.

Skills

ML experience

Python

NLP

PyTorch

Hugging Face

LangGraph

AutoGen

CrewAI

Function calling

Docker

Kubernetes

CI/CD

RAG architecture

Software engineering

Git

Education

Bachelor's degree in CS/Math/Physics

Master's or PhD preferred

Tools

Git

CI/CD

Docker

Kubernetes

Job description

Lenovo is seeking a technical lead to own the full agentic stack—from LLM orchestration to production deployment. You will design and ship agentic pipelines, remember and reason over data, and integrate tools across multi-step tasks.

You will build evaluation frameworks for reliability and safety, maintain RAG systems and vector stores, and scale deployments in production. Collaboration with AI engineers, ML scientists, and product managers is essential.