Staff IT AI Implementation Engineer

PROCEPT BioRobotics•Headquarters, KY

53d•$163,910 - $192,840

About The Position

This is a unique opportunity to help define how AI works at PROCEPT BioRobotics from the ground up. As the AI Implementation Engineer within the IT department, you will own the company's AI services ecosystem — from enterprise platforms like Claude and ChatGPT to the agents and automated workflows that make AI tangible across the business. This role sits squarely at the intersection of technical execution and strategic influence: you will not only build and maintain the AI infrastructure that powers every department, but also serve as the company's primary point of contact for all AI needs and play a meaningful role in shaping the governance policies that determine how AI is used responsibly at Procept. If you are someone who gets energized both by deploying a production-ready AI agent and by helping a business unit understand what they can and cannot do with AI — and why — this role was built for you.

Requirements

3–5 years of experience in IT, systems administration, or a related technical discipline.
1–3 years of hands-on experience working with AI models, large language models, frontier AI services, or AI agent development.
Foundational understanding of networking and cybersecurity principles to safely deploy and integrate AI services within the enterprise environment.
Experience in managing virtualized environments. Such as hypervisors like ESX, HyperV, Proxmox, etc. Or containers like Docker/Kubernetes.
Demonstrated experience deploying and managing AI tools or services in an enterprise environment.
Proven ability to work cross-functionally and translate technical concepts for non-technical stakeholders.
Experience contributing to technical policies, governance documentation, or compliance programs.
Ability to independently manage multiple concurrent projects and priorities in a dynamic, fast-moving environment.
Experience with cloud platforms (AWS, Azure, or GCP) and their AI/ML service offerings

Nice To Haves

Claude Certified Associate or Claude Certified Developer – Foundations (Anthropic's official certification, delivered via Pearson VUE)
AWS Certified AI Practitioner (AIF-C01), or AWS Certified Generative AI Developer – Professional for candidates with deeper hands-on build experience
CompTIA AI+ or equivalent vendor-neutral AI credential
Microsoft Azure AI Fundamentals (AI-901)
Familiarity with AI agent frameworks such as LangChain, LangGraph, AutoGen, or similar orchestration tooling
Familiarity with Ollama, OpenWebUI, or any similar opensource tools for deploying local LLM models.
Any examples of personal projects/implementations of AI usage outside of work.

Responsibilities

Administer and manage enterprise AI services — including frontier LLM platforms such as Claude Enterprise and ChatGPT Enterprise — ensuring availability, security, licensing compliance, and appropriate access controls.
Design, build, deploy, and maintain AI agents and automated workflows that solve real business problems across departments.
Serve as the company's primary point of contact for AI-related requests, project needs, and questions from all business units.
Partner with department stakeholders to assess AI use cases, evaluate feasibility, scope solutions, and deliver implementations that align with IT standards and business objectives.
Collaborate with IT leadership to develop, maintain, and continuously improve the company's AI governance policies and acceptable use standards.
Conduct ongoing compliance reviews ensuring all departments and AI-connected services adhere to established IT AI policies and security requirements.
Evaluate and vet emerging AI tools, platforms, and services, providing recommendations for adoption, integration, or restriction.
Coordinate with AI platform vendors on licensing, entitlements, technical onboarding, and support escalations.
Provide training, guidance, and enablement to employees across the organization on the effective and compliant use of approved AI tools.
Track AI usage, cost allocation, and service health across managed platforms and surface insights and recommendations to leadership.