Lead AI Engineer

Description

The company is a large healthcare company that delivers insights, products, and services aimed at making quality care more accessible and affordable.

The AI Enablement team drives company-wide adoption of generative AI. It covers platform engineering, governance, and hands-on delivery of AI-powered products and internal tools, and builds the infrastructure, integrations, and knowledge systems that let engineering and business teams adopt large language models safely at scale.

This Lead AI Engineer is the senior technical authority on the team and sets the architecture that Senior and Associate AI Engineers build against. The role sits one level above Senior AI Engineer and owns the technical design of agentic and LLM-powered systems built on large language model APIs, including retrieval-augmented generation (RAG), tool and connector integration, and evaluation and governance tooling, in a highly regulated healthcare setting.

You will set architecture standards for AI-embedded product delivery, mentor Senior and Associate engineers, and present AI platform architecture to engineering leadership and Legal, Compliance, and Privacy.

Success is measured by how reliable, secure, and scalable the shipped AI systems are, how quickly Senior and Associate engineers adopt the standards you set, and how much you shape AI platform decisions across the organization.

Responsibilities

• Define the architecture and roadmap for LLM and agentic systems, covering model routing across cloud AI platforms, connector and integration patterns, RAG pipelines, and prompt and evaluation frameworks.
• Lead architecture reviews for new AI capabilities and confirm designs meet enterprise standards for security, privacy, and data provenance.
• Design shared AI platform capabilities, including knowledge retrieval, citation and traceability of AI outputs, and permission-aware data access.
• Build high-complexity components yourself on priority AI delivery initiatives, where a working system is the deliverable rather than a design document.
• Set coding, architecture, and evaluation standards for Senior and Associate AI Engineers, run design reviews, and pair with them on complex agentic workflows.
• Set up secure, observable, cost-attributed AI operations for production LLM systems, including usage and spend attribution, abuse monitoring, model evaluation, and rollback strategy.
• Work with engineering managers, product, platform teams, and Legal and Compliance to match AI architecture decisions to delivery timelines and enterprise standards.
• Assess foundation model providers and AI tooling vendors, and recommend whether to build or buy and which models to use.
• Ensure AI systems that handle protected health information (PHI) meet regulatory and enterprise governance requirements.
• Represent AI architecture in cross-company forums and shape enterprise-wide AI platform direction.
• Candidates living in the Columbus, OH metropolitan area will be given priority.

Requirements

• A bachelor's degree or equivalent experience, usually with 8+ years of software engineering and 3+ years building AI/ML, LLM, or NLP systems in production. Fewer years are acceptable with a relevant master's or doctorate.
• Demonstrated technical leadership one level above Senior AI Engineer, such as owning architecture for a team or platform area rather than a single feature.
• Deep hands-on experience building production LLM applications, including prompt engineering, RAG and GraphRAG, agentic workflows, tool use, and evaluation harnesses.
• Experience with commercial LLM APIs (for example Anthropic or OpenAI) and tool-integration protocols such as Model Context Protocol (MCP).
• Strong software engineering fundamentals in Python and/or Java, REST APIs, event-driven architecture, microservices, and cloud-native design. Azure is preferred; AWS or GCP is acceptable.
• Production experience with vector and graph retrieval systems, embeddings, and knowledge-base architectures at scale.
• Working knowledge of LLMOps and MLOps: usage attribution, cost and spend monitoring, abuse detection, and model evaluation pipelines.
• Ability to set architecture standards and mentor engineers through design reviews and pairing, not only through documentation.
• Familiarity with regulated healthcare data and AI governance frameworks, such as model risk review, connector approval tiers, and PHI handling. This is a plus.
• Preferred: an advanced degree (M.S. or higher) in computer science, AI/ML, or a related technical field.
• Preferred: prior experience leading AI/ML platform or applied-AI teams in healthcare IT or another highly regulated industry.
• Preferred: experience modernizing legacy systems toward AI-embedded workflows, such as agent-assisted development, AI-powered risk scoring, or AI-driven automation.
• Preferred: experience with Confluence and Jira-based engineering governance and enterprise architecture review boards.
• Strong awareness of the fast-moving GenAI and agentic landscape and a habit of continuous learning.
• Experience managing relationships with AI tooling vendors and model providers.
• Clear written and verbal communication for both technical and executive audiences.

Conditions

Base pay range is $155,300 to $258,800.

Pay is set by factors including performance, experience and skills, equity, regular job market reviews, and geographic market. Annual bonus or long-term incentive opportunities may also be offered in addition to base pay.

Sponsorship is not available for this role, now or in the future.

Equal opportunity employer.

Benefits

• Competitive compensation package as part of Total Rewards. Benefit details are available from the company's benefits information.