• Design, build, and maintain agentic systems and LLM-powered applications that automate healthcare workflows, data pipelines, and clinical decision support — from conception through production deployment • Build and orchestrate agents using LLM APIs (OpenAI, Anthropic, etc.) and agentic frameworks (LangChain, LangGraph, CrewAI, or custom orchestration) to solve complex, multi-step healthcare problems • Develop prompt libraries, agent instructions, and reusable "skills" that improve agent accuracy, consistency, and reliability across different use cases and data domains • Build validation and confidence-scoring layers that flag low-confidence agent decisions for human review before production deployment; establish guardrails and review workflows for agent-authored code and outputs • Own end-to-end delivery of AI-automated systems — from problem scoping and requirements gathering through agent development, testing, and validated production deployment • Implement rigorous evaluation and QA frameworks for agentic systems — including golden datasets, test cases, output validation, hallucination detection, and regression testing • Establish and maintain evaluation metrics for agent performance, reliability, and clinical appropriateness; measure agent accuracy, hallucination rates, clinical validity, and real-world impact • Implement observability, evaluation, and regression testing frameworks specific to agentic systems — decision tracing, lineage logging, and performance tracking • Collaborate with data engineering and platform teams to integrate agent-built outputs (dbt models, transformation logic, recommendations) into existing data architectures and clinical workflows • Ensure all agentic systems comply with healthcare regulations (HIPAA, FDA guidance on AI/ML) and responsible AI practices — including explainability, auditability, and clinician trust • Continuously evaluate new LLM models, agent frameworks, prompt engineering techniques, and tooling; recommend adoption or migration based on healthcare-specific requirements (accuracy, cost, latency, regulatory alignment) • Partner with data engineering to establish robust data validation and input validation layers for agents — agents are only as good as the data they operate on • Lead experimentation and measurement of AI-automated systems impact on speed, quality, compliance, and cost across healthcare workflows • Document agent architectures, prompt strategies, evaluation frameworks, and best practices for both technical and non-technical stakeholders • Mentor AI Connector Engineers and other team members on agentic development patterns, LLM-powered application design, and responsible AI practices • Work on-call as needed to support production agentic systems, troubleshoot agent issues, and respond to performance degradation or hallucination detection

• Build and enhance AI-powered SaaS applications and internal platforms. • Translate founder ideas, workflows, and storyboards into production-ready product features. • Develop and maintain frontend and backend systems across multiple SaaS products. • Integrate third-party APIs and external services. • Develop AI-powered workflows using modern LLM technologies.

Role Description If you're a full-stack engineer who is already using tools like Claude Code, Codex, Cursor, or similar AI-assisted workflows to ship real product work — and you care enough to review, test, and secure every line those tools help produce — this is a chance to build meaningful software in a regulated, mission-critical industry. You'll join a PE-backed B2B SaaS company modernizing aviation compliance, credentialing, training, and access-control workflows used by airports and aviation organizations. This is a hands-on full-stack engineering role for an early-to-mid-career engineer who wants ownership, mentorship, and a clear path to grow. The company is moving legacy acquired products toward a newer unified platform while building new capabilities in areas like: - Drug program workflows - Fingerprinting - Background checks - Credentialing - Compliance operations The work is practical, high-impact, and directly tied to reducing key-person risk while accelerating a major platform transformation. You'll work closely with a hands-on Head of Engineering who wants to mentor engineers, raise the technical bar, and build a modern AI-forward engineering culture. What You'll Own - Build and ship full-stack product features - Develop new product capabilities across full-stack web applications - Work primarily in modern JavaScript/TypeScript environments, with React and Next.js strongly preferred - Help migrate important workflows from legacy systems into a newer platform architecture - Build features that support regulated aviation workflows such as compliance tracking, credentialing, background checks, fingerprinting, and related operational processes - Use AI-assisted development responsibly - Review AI-generated code for correctness, maintainability, security, edge cases, and bugs - Validate outputs through thoughtful testing, manual review, and clear technical reasoning - Contribute to team execution and reliability - Communicate progress, questions, and blockers proactively - Work with teammates to improve code quality, documentation, testing practices, and delivery flow - Help create a more energized, collaborative engineering team after a period of change What Makes You a Strong Fit - You have strong full-stack engineering fundamentals and enjoy solving product problems, not just writing code - You have experience shipping features using AI-assisted or agentic development workflows such as Claude Code, Codex, Cursor, or similar tools - You can explain how you validate AI-generated code before it reaches production - You are comfortable with JavaScript or TypeScript; TypeScript, React, and Next.js are especially relevant to the new platform work - You care about clean, maintainable code and understand the importance of testing - You are coachable, direct, and comfortable receiving feedback from senior engineering leadership - You can work with urgency while still being careful in regulated, compliance-oriented product domains - You are motivated by visible impact in a small-to-mid-sized company environment Experience Level - This role is open to a range of early-to-mid-level engineers - Roughly 3–5 years of software engineering experience is a strong fit - Candidates with 1–2 years of experience or strong recent graduate backgrounds may also be considered if they show strong technical signal, hunger, coachability, and shipped project work - Nonlinear career paths, startups, project-based work, or recent transitions are not automatic red flags, but you should be able to clearly explain what you built, why you moved, and what will make you successful here Why This Role Is Interesting - You'll work on real software for real customers in a regulated industry where accuracy and reliability matter - You'll be part of a platform modernization effort, not just incremental feature maintenance - You'll get direct access to engineering leadership and a strong mentoring environment - You'll help shape how AI-assisted engineering is used responsibly inside a product team - You'll have room to make a visible impact in a PE-backed SaaS company with meaningful growth expectations Compensation, Location, and Work Model - Base salary range: $100,000–$145,000, based on experience and level - Remote role based in the United States - Full-time position - Interview process includes video interviews A Note on the Environment This is not a purely greenfield startup role. There is legacy complexity, regulated workflow complexity, and a real need for engineers who can execute with focus. The right person will be excited by that mix: modern AI-assisted development, practical full-stack product work, and the chance to help move a business-critical platform forward. If you're hungry to build, excited about the future of software development, and ready to use AI tools with real engineering discipline, we'd like to hear from you. Apply now and get a response within 24 hours.

• Architect GitLab's AI-native learning ecosystem, including adaptive learning paths, coaching agents, bots, intelligent recommendations, and automated content workflows. • Lead GitLab's company-wide AI fluency and enablement strategy in partnership with the Enterprise AI team, from baseline literacy through builder capability. • Embed AI fluency into onboarding, leadership development, and role-specific learning pathways. • Own the multi-year learning platform strategy and roadmap, including platform evaluations, migrations, integrations, and capability expansions. • Drive operational excellence across the Talent Management & Development team by managing the product roadmap, release schedule, intake processes, documentation, automations, and cross-functional coordination. • Partner with People Technology as the technical lead for Talent Development, translating learner and business needs into architecture briefs and co-building agents, workflows, and platform integrations. • Partner with People Analytics to define measurement infrastructure and dashboards for learning engagement, AI adoption, behavior change, capability growth, and program return on investment. • Lead global compliance training and vendor management, including audit readiness, negotiations, renewals, quarterly business reviews, budgets, adoption targets, and investment cases.