Sr AI Technical Program Mgr I

ForeFlight•Austin, TX

10d•Hybrid

About The Position

Jeppesen ForeFlight builds industry-leading aviation software used by pilots, aircraft operators, and major airlines worldwide. As a high-growth, private equity-backed company, we are focused on scaling our operations, strengthening our financial infrastructure, and driving operational excellence across the business. Our team combines deep domain expertise with a collaborative, high-performance culture to solve complex challenges and support continued growth. We're hiring a hands-on AI Adoption Champion to drive responsible, measurable adoption of AI tools across our ~3,000-person aviation software organization. This is a hybrid technical-and-programmatic role: you'll drive the platform configuration, day-to-day operations, and be the visible internal champion who makes AI useful, safe, and a little bit fun for engineers, product, customer success, legal, and back-office teams alike. You'll partner closely with IT/Security, Legal, HR, and our business unit leaders. The ideal candidate would thrive spearheading cross-functional AI adoption strategies with their hands-on knowledge of AI systems, and consumable communications to C-level executives and early career developers alike.

Requirements

3+ years in a technical role that touched both platform/admin operations and internal stakeholders — examples: SaaS admin (Salesforce, Workday, Atlassian), DevOps/SRE with internal-tools focus, sales engineer, solutions architect, or enterprise IT engineer
Hands-on experience configuring at least one enterprise AI platform (Claude, ChatGPT Enterprise, Copilot, Gemini, or similar) including SSO, permissions, and admin APIs
Comfort with cloud infrastructure concepts (AWS or Azure), identity (Okta/Entra/OIDC), and basic scripting (Python, JavaScript, or PowerShell) — enough to debug an integration, not necessarily ship production code
Demonstrated ability to design and run an internal program — not just deliver tickets — including communications, training, and measurement
Strong written communication; can write a runbook, a launch announcement, and a board-ready slide with equal comfort
Data fluency: you can build a dashboard, interpret a funnel, and tell the difference between a usage spike and a usage outcome

Nice To Haves

Have spent the last 6-12 months living alongside AI tools and workflows with an early-adopter mindset (knowledge graphs, skills and extensions, agentic workflows, continuous improvement)
Experience in a regulated industry (aviation, aerospace, financial services, healthcare) and an instinct for safety-critical software environments
Familiarity with enterprise AI deployment patterns: Bedrock, Azure OpenAI, API Gateway/Lambda authorizer architectures, CloudWatch/observability
Background in change management, learning & development, or developer relations
Experience designing gamification or community programs that survived past the launch high
Working knowledge of AI governance frameworks such as ISO 42001 and EU AI Act implications
Experience operating across geographies, especially EU works council environments

Responsibilities

Build and run a multi-channel adoption program: office hours, internal community (Slack/Zoom/Confluence), demos, training, and on-demand learning paths
Design gamification mechanics — challenges, leaderboards, badges, prompt-of-the-week, team-vs-team competitions — tied to real productivity outcomes, not vanity activity
Identify and develop "AI champions" inside each business unit; equip them with playbooks, prompt libraries, and use-case templates
Translate wins into stories: surface high-impact examples and amplify them through internal comms, all-hands, and exec updates
Own the configuration of our enterprise AI platforms (Claude, Copilot, and adjacent tools): user provisioning, SSO/SCIM, permissions, data controls, retention policies, content filters
Stand up and maintain integrations with our identity, observability, and productivity stack (Okta, AWS, Slack, M365, Jira, etc.)
Configure guardrails aligned to our AI governance framework, including data classification, sensitive-data handling, and region-specific requirements
Maintain a sandbox/eval environment for testing new models, prompts, and connectors before broad rollout
Monitor and communicate the art of the possible using emerging foundation models, skills, MCPs, and related technologies
Serve as the AI-tooling subject-matter expert during incidents; IT, Business Applications, and Customer Support own incident response and RCA, with this role looped in on outages, degradations, and post-mortems
Track outage and degradation patterns and feed them back into vendor escalations and adoption planning
Manage vendor relationships, support tickets, license utilization, and renewal readiness
Keep an internal status page and known-issues log so users always know what's working
Build role-specific learning paths for non-technical functions, e.g., plain-language curricula with realistic, function-specific examples (a CS rep summarizing tickets, a legal analyst reviewing a vendor MSA, an FP&A analyst drafting variance prose)
Run guided, hands-on workshops and "first-use" sessions; Default to live coaching over self-service docs for many of these audiences
Curate role-tuned, organizationally available tools including prompt libraries, Skills, MCPs, and git-backed Plugins that are easy to install and separately maintained from the developer-oriented material
Identify and develop non-technical AI champions inside each function so support is local, contextual, and trusted
Partner with HR and Communications to weave AI fluency into onboarding for every new hire, regardless of role
Define and instrument the adoption KPIs that matter: active usage, depth of use, use-case mix, productivity outcomes, and ROI signals — not just seat counts
Build AI-assisted dashboards that segment by org, role, geography, and use-case taxonomy
Contribute to monthly and quarterly readouts for the AI Adoption team and executive leadership; partner with Finance on cost-per-active-user and ROI modeling
Run periodic surveys and qualitative research to complement the quant picture
Work with Legal, Privacy, and HR on policy, training, and regional rollout (including co-determination processes where applicable)
Partner with IT/Security on access reviews, DLP, and audit readiness
Feed learnings back into the AI governance framework and use-case taxonomy