Jobs / United States / Humana INC
Lead AI Engineer - NBA
Humana INC · 🇺🇸 Remote Nationwide · Remote
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 260 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 130 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Humana INC paid sponsored hires in similar roles34 certified filings for “Senior Software Engineer” (Software Developers) in KY: $124k–$136k, median $131k. Most were filed at wage level IV (86%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Humana INC
The US Department of Labor certified 260 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 130 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Humana INC →
About the role
Become a part of our caring community The Lead AI Engineer owns the design and delivery of the NBA platform's agentic AI capabilities. This is a hands-on engineering role first - you are expected to be a significant individual contributor building production AI systems every sprint. You also lead a small team of engineers and contractors focused on bringing AI-powered workflows and intelligent analysis into the platform's user-facing experiences. This role is forward-looking: you are building the foundation for how AI augments and automates decision-making across the platform. What You'll Own • Agentic AI delivery - design, build, and own AI agent workflows that surface intelligent analysis, recommendations, and automation across the NBA platform's user interfaces and internal tooling • Hands-on development - actively write and ship production code alongside your team; this is not a supervisory role • AI workflow design - architect multi-step agentic pipelines that integrate with the platform's data layer, decisioning outputs, and activation channels to deliver meaningful AI-driven experiences • LLM integration - own the integration of large language models (Claude, GPT, or equivalents) into platform workflows, including prompt engineering, tool use, structured output handling, and context management • User-facing AI experiences - partner with frontend and product teams to bring AI-powered analysis and recommendations into the Command and Control UI and other operator-facing surfaces • Evaluation and reliability - build evaluation frameworks to measure AI output quality; ensure agentic workflows are reliable, auditable, and safe in a regulated healthcare environment • Contractor management - manage AI engineering contractors within your pod; set expectations, review work, and hold the team to quality standards • AI productivity tooling - model best practices for AI-assisted development (Claude, Copilot, etc.) across the broader engineering organization Use your skills to make an impact Required Qualifications • 6+ years of software or ML engineering experience with at least 1-2 years focused on LLM integration, agentic AI, or AI product engineering • Hands-on experience building production agentic systems - multi-step pipelines, tool orchestration, structured output handling, and multi-turn conversation management • Experience with RAG patterns and vector search - you have built retrieval-augmented systems in production, not just read about them • Strong Python engineering skills; comfortable building and shipping production-grade AI systems, not just prototypes • Experience integrating LLM APIs (Anthropic, OpenAI, or equivalents) into real applications • Proven ability to lead a small team or pod through delivery and manage contractor resources • Strong judgment on when AI is the right tool and when it is not - especially in a regulated, member-facing context • Clear communicator - you can explain an agentic architecture to an engineer and a product owner without losing either of them • Deep fluency with AI productivity tools (Claude, Copilot, or similar) - this is table stakes for this role Preferred Qualifications • Experience orchestrating multi-agent systems - coordinating multiple AI agents across complex, long-running workflows • Experience integrating agentic AI into bespoke enterprise software and connecting to external systems like Salesforce, Adobe Experience Platform, claims platforms, or similar • Experience building AI-powered analytics or insight surfaces in a business intelligence or operational UI context • Background in healthcare, insurance, or other regulated industries where AI outputs carry compliance risk • Experience with AI evaluation frameworks and techniques for measuring out