AI Engineer

AI Engineer

Location: Chicago, IL – Hybrid (3 Days Onsite / 2 Days Remote)
Work Arrangement: Local candidates only – Chicago area
Initial Office: Buffalo Grove, IL
Future Office: 300 North LaSalle, Chicago – Expected relocation in January
Duration: 12–24 Months
Interview Process: Multiple Video Interviews, including Craft Exercise and Coding/Technical Exercise

Position Overview

We are seeking a highly skilled AI Engineer to join the AI Platform & Enablement team supporting multiple product lines across the organization. This team is responsible for building scalable AI capabilities, establishing common patterns and guardrails, enabling product teams, and providing governance across the enterprise AI ecosystem.

The ideal candidate will combine strong software engineering fundamentals with hands-on production AI experience. We are looking for someone who has built and deployed AI solutions into production and understands how to maintain quality, reliability, observability, and governance in non-deterministic AI systems.

This is a platform-focused engineering role. The successful candidate should think beyond building a solution for a single product and instead understand how to create reusable AI platforms, frameworks, tooling, patterns, and guardrails that can be adopted across multiple teams.

Key Responsibilities

• Design, develop, and deploy production-grade AI and GenAI solutions that can be leveraged across multiple product lines.
• Build reusable AI platform capabilities, frameworks, tools, patterns, and guardrails to accelerate AI adoption across the organization.
• Develop and maintain AI/agentic systems with a strong focus on quality, reliability, scalability, security, and operational excellence.
• Establish approaches for evaluating and maintaining the quality of non-deterministic AI/LLM-based systems.
• Develop evaluation frameworks, testing strategies, monitoring, and quality-management processes for AI solutions.
• Apply systems-level thinking to AI architectures and identify cross-cutting technical challenges across multiple solutions.
• Partner with AI architects, product teams, engineers, data scientists, and other stakeholders to enable successful AI adoption.
• Help product teams adopt standardized AI development practices, SDLC processes, patterns, and governance standards.
• Solve complex AI engineering problems and provide scalable solutions that can be reused across the organization.
• Support governance and audit activities to ensure AI solutions meet established engineering and quality standards.
• Monitor the health and performance of deployed AI solutions and identify opportunities for improvement.
• Participate in AI enablement activities, including technical workshops, roadshows, documentation, and knowledge-sharing sessions.
• Help reduce dependency on centralized teams by enabling product teams to independently build and operate AI capabilities.
• Stay current with emerging AI engineering, agentic AI, LLM, evaluation, and platform technologies.

Required Qualifications

• Strong software engineering background with demonstrated experience building production-grade AI solutions.
• Hands-on experience designing, developing, and deploying AI/ML/GenAI applications into production.
• Strong Python development experience.
• Experience working with modern AI/LLM technologies and AI application stacks.
• Experience developing or supporting agentic AI, AI agents, LLM applications, or intelligent automation solutions.
• Strong understanding of AI quality management, evaluation, testing, monitoring, and reliability.
• Ability to address the challenges of maintaining quality and consistency in non-deterministic AI systems.
• Strong systems-thinking ability with experience solving cross-cutting, enterprise-level engineering problems.
• Experience designing solutions that are scalable, reusable, maintainable, and platform-oriented.
• Strong understanding of software engineering principles, architecture, SDLC, CI/CD, testing, and production operations.
• Demonstrated enablement mindset with the ability to build capabilities that empower other engineering/product teams.
• Excellent communication and collaboration skills.

Preferred Qualifications

• Experience building AI enablement platforms or enterprise AI infrastructure.
• Experience with multi-agent architectures and agentic AI pipelines.
• Experience with Intelligent Document Processing (IDP) solutions.
• Background as a Data Scientist who transitioned into AI/software engineering or as a Full Stack Engineer who moved into AI/GenAI engineering.
• Experience with prompt engineering and prompt evaluation.
• Experience with AI infrastructure, AI platform engineering, or AI architecture.
• Experience with .NET/C# and/or React/Angular.
• Experience developing reusable AI frameworks, libraries, APIs, or developer tooling.
• Experience establishing AI governance, standards, guardrails, and organizational best practices.

Ideal Candidate Profile

The ideal candidate is an AI-focused software engineer who has already built and operated AI solutions in production. They should be comfortable working across the entire AI engineering lifecycle—from architecture and development through evaluation, deployment, monitoring, governance, and continuous improvement.

We are especially interested in candidates who can demonstrate:

• Production AI experience, not just prototypes or research.
• A strong understanding of AI quality and evaluation.
• Experience with agentic AI or LLM-based systems.
• A platform mindset rather than a single-product mindset.
• The ability to create reusable patterns and enablement capabilities for other teams.
• Strong engineering fundamentals and systems-level thinking.
• The ability to operate effectively in a rapidly evolving AI environment.

Team & Platform Mission

The AI Platform & Enablement team serves as a centralized enablement and governance function supporting approximately 12 product lines. The team builds common AI capabilities and establishes standards that product teams can consume and operate independently.

The team will focus on:

• Building common AI patterns, tooling, frameworks, and guardrails.
• Solving complex cross-cutting AI engineering challenges.
• Establishing and enforcing AI engineering and governance standards.
• Improving the health, quality, and reliability of the AI solution fleet.
• Enabling product teams through technical guidance, training, and roadshows.
• Increasing product-team autonomy and reducing reliance on centralized "Tiger Team" intervention.

The long-term vision is to create a self-sufficient AI platform and enablement function that allows product teams to build, deploy, govern, and operate AI solutions independently while maintaining enterprise-level quality and standards.

Work & Travel Requirements

• Candidates must be local to the Chicago metropolitan area.
• Hybrid schedule: 3 days onsite and 2 days remote per week.
• Candidates must initially be willing to work from the Buffalo Grove, IL office.
• The team is expected to relocate to 300 North LaSalle, Chicago, in January.
• Must be willing to travel internationally a few times per year, if required.