Principal AI Engineer

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The Principal AI Engineer serves as a senior technical authority responsible for guiding Humana’s enterprise platform strategy and engineering execution, with a dedicated focus on agentic AI systems—AI solutions capable of autonomous action, complex reasoning, and orchestrating tasks across multiple environments. This role leads the design, development, and operation of foundational agentic AI platforms, tools, and services that empower development teams to build, deploy, and operate agent‑driven applications efficiently, securely, and reliably.

As a principal‑level contributor, this position influences architectural direction for core infrastructure supporting agentic AI workloads, drives innovation in developer experience and automation—including agent orchestration and human‑AI collaboration—mentors engineering teams, and ensures that platform solutions are robust, scalable, secure, and aligned with Humana’s enterprise agentic AI technology roadmap. The ideal candidate blends deep full‑stack engineering expertise with hands‑on proficiency in cloud infrastructure, automation, developer tooling, and advanced agentic AI platform capabilities—capable of shaping long‑term vision while delivering high‑quality, production‑ready agentic AI platforms.

• Lead the design, development, and evolution of internal developer platforms tailored for agentic AI use cases, focusing on self‑service capabilities, intelligent automation, and an optimized developer experience for building and deploying AI agents.
• Define architectural patterns and best practices for infrastructure, agent deployment and lifecycle management, and operational excellence, ensuring security, scalability, and cost‑efficiency across agentic AI workloads.
• Evaluate, select, and integrate foundational agentic AI technologies, including cloud‑based agent orchestration frameworks, container orchestration (Kubernetes), infrastructure‑as‑code (IaC) tools, agent workflow orchestration platforms, and observability solutions for agentic AI systems.
• Drive the adoption of platform‑as‑a‑service (PaaS), infrastructure‑as‑code (IaC), and agent‑as‑a‑service principles to standardize and automate environment provisioning, agent training, and agent serving.

• Architect, deploy, and manage core infrastructure and platform services on cloud platforms (GCP preferred), leveraging agentic AI and ML services such as Vertex AI, Compute Engine, GKE, and Cloud Storage.
• Design and implement advanced networking, security controls, and access management (IAM) for agentic AI systems, supporting compliance and responsible AI guidelines.
• Establish and manage comprehensive monitoring, logging, tracing, and alerting systems for agent workflows, models, and applications, ensuring the reliability, performance, and health of both platform and hosted agentic AI solutions.

• Build and maintain robust CI/CD and agentic AI pipeline automation (e.g., Git Lab CI/CD, Jenkins, Kubeflow, ArgoCD) for automated testing, deployment, release management, and agent lifecycle management.
• Develop internal tools, APIs, and services that abstract agentic AI infrastructure complexity, enabling application and data science teams to self‑service their agent/agent group training, deployment, and monitoring needs.
• Champion best practices for code quality, testing, security scanning, and automated deployments, integrating agentic AI‑assisted developer tools (e.g., Git Hub Copilot, Claude Code, Gemini CLI) into platform offerings.
• Provide guidance and support to engineering and data science teams on leveraging platform agentic AI capabilities to improve development workflows and operational posture.

• Develop scalable backend services and APIs using Python, specifically designed to power agentic AI platform functionalities and developer tools.
• Design and implement REST‑compliant or gRPC endpoints with versioning, comprehensive error handling, and clear documentation (e.g.,…