Principal AI Engineer Job Louisville area,Kentucky USA,Software Development

Humana is hiring a Principal AI Applied Engineer to define long-term AI technical strategy and architecture for enterprise healthcare AI-enabled products, while also contributing hands-on through prototyping, experimentation, and software development. This role shapes model strategy, platform investments, standards, governance, and cross-team adoption to deliver scalable, reliable, responsible production AI systems.

Key Responsibilities

Define and lead the long-term technical strategy, reference architecture, and technology roadmap for AI-enabled products and platforms.
Set architectural patterns and best practices for retrieval systems, LLM orchestration, agentic workflows, human-in-the-loop processes, evaluation frameworks, and AI governance.
Drive organization-wide technical decisions on model strategy, vendor selection, platform investments, build-versus-buy choices, and shared infrastructure capabilities.
Design scalable architectures that balance reliability, performance, security, compliance, maintainability, and cost efficiency
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Establish engineering standards for evaluation, observability, model lifecycle management, responsible AI practices, and operational excellence.
Build prototypes, proofs of concept, and reference implementations to reduce risk in emerging technologies and strategic platform investments.
Lead cross-functional architecture reviews and provide technical guidance across multiple engineering teams.
Mentor Lead Engineers, Senior Engineers, and emerging technical leaders through architecture reviews, design feedback, coaching, and technical sponsorship.
Develop and communicate technical roadmaps aligned to business strategy and organizational objectives.
Partner with executive leadership to translate business priorities into technical strategies, and communicate technical opportunities, risks, and tradeoffs.
Represent engineering in strategic vendor evaluations, compliance discussions, governance reviews, and enterprise planning initiatives.
Drive adoption of common platforms, standards, and engineering practices across teams.
Ensure AI systems are designed and operated to meet privacy, security, compliance, governance, and auditability requirements.

Required Qualifications

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
12+ years of software engineering experience, including extensive experience designing and operating large-scale distributed systems.
Proven experience designing, delivering, and operating production AI-enabled platforms or products at enterprise scale.
Deep expertise in large language model technologies, AI platform design, retrieval-augmented generation (RAG), agentic systems, orchestration frameworks, evaluation methodologies, and AI operations.
Experience designing cloud-native architectures and highly scalable, resilient systems.
Demonstrated ability to define technical direction that influenced teams, organizations, or product portfolios beyond direct reporting relationships.
Experience establishing engineering standards, architectural frameworks, or technical platforms adopted across multiple teams.
Strong understanding of AI operational challenges, including model reliability, latency, cost optimization, governance, observability, and risk management.
Demonstrated success influencing executive stakeholders and driving organizational decisions through technical leadership and evidence-based recommendations.
Expert-level proficiency in Python and/or Type Script/Java Script .
Ability to prototype, debug, review, and contribute code across multiple layers of the technology stack.

Preferred Qualifications

Experience leading AI platform strategy across multiple business units or product organizations.
Experience developing enterprise standards for AI evaluation, testing, observability, governance, and responsible AI practices.
Expertise evaluating and managing AI vendor ecosystems across multiple model providers and platform technologies.
Experience building shared AI infrastructure and platform capabilities consumed by multiple engineering teams.
Experience with agentic architectures, Model Context Protocol (MCP), workflow orchestration, autonomous systems, and emerging AI frameworks.
Experience operating AI systems at significant scale while managing performance, reliability, and cost tradeoffs.
Experience in regulated industries such as healthcare, financial services, life sciences, or government.
Deep understanding of privacy,…