AI Engineering Tech Lead

Hudson Manpower•Schaumburg, IL

2d•Onsite

About The Position

Seeking a highly skilled and passionate AI Engineering Tech Lead to lead a team of 10+ engineers in designing, developing, and deploying next-generation AI solutions. This role requires a hands-on technical leader with deep expertise in Generative AI, Agentic AI systems, Large Language Models (LLMs), and modern software engineering practices. The ideal candidate will combine strong leadership capabilities with technical excellence, driving innovation while mentoring engineers and delivering scalable AI-powered applications that create business impact.

Requirements

8+ years of software engineering experience with at least 2+ years leading engineering teams.
Strong hands-on experience with Python development.
Deep expertise in Generative AI, LLMs, and Agentic AI architectures.
Experience with frameworks such as: LangChain LangGraph AutoGen CrewAI Semantic Kernel
Experience building RAG (Retrieval-Augmented Generation) solutions.
Strong knowledge of prompt engineering, vector databases, embeddings, and AI orchestration.
Experience integrating AI applications with REST APIs, databases, and external systems.
Familiarity with cloud platforms such as AWS, Azure, or GCP.
Strong understanding of software design patterns, system architecture, and microservices.
Experience with CI/CD pipelines and MLOps practices.

Nice To Haves

Experience leading AI transformation initiatives in large enterprise environments.
Exposure to healthcare, diagnostics, life sciences, or regulated industries.
Knowledge of AI governance, responsible AI, and model evaluation frameworks.
Experience with Kubernetes, Docker, and distributed systems.

Responsibilities

Lead, mentor, and manage a team of 10+ AI/Software Engineers.
Architect and develop enterprise-grade AI and Agentic AI solutions.
Design and implement applications using Large Language Models (LLMs) and Generative AI technologies.
Drive end-to-end AI product development from proof-of-concept to production deployment.
Collaborate with business stakeholders, architects, product owners, and engineering teams to define AI roadmaps and technical strategies.
Build AI agents capable of reasoning, planning, and autonomous task execution.
Integrate AI solutions with APIs, databases, cloud platforms, and enterprise applications.
Establish coding standards, best practices, and AI governance frameworks.
Conduct code reviews and provide technical guidance to engineering teams.
Evaluate emerging AI technologies and recommend innovative solutions.
Ensure scalability, security, performance, and reliability of AI platforms.