AI SaaS Product Manager

Position Overview

We are seeking an AI SaaS Product Manager to own the released application portfolio built in-house by the AI & Autonomy division — the AI-powered web, mobile, and spatial applications used daily by manufacturing, satellite operations, analytics, and executive teams, along with AI capabilities delivered to telecom partners. This person is the connective tissue between the engineers who build these applications, the internal customers who depend on them, and the IT organization that helps support them, deciding what ships next based on real usage and ensuring everything that ships is documented, learnable, and supportable. As the organization matures its operational support model for AI applications, this role also supports the Chief of AI and Autonomy in executing the transition roadmap that moves these applications from build-team support into steady-state operations. This is a hands-on, writing-heavy product role for someone fluent enough in AI/ML systems to document them accurately, and user-obsessed enough to measure success by adoption, time-to-resolution, and how rarely people need to ask for help. 

Key Responsibilities:

• Own the post-launch product lifecycle for the AI division's released applications — prioritizing enhancements, defects, and retirements based on usage data, user feedback, and business impact — and maintain a published roadmap internal customers can plan against.
• Partner daily with the full-stack, ML, and data science engineering teams to translate user needs into clear requirements, user stories, and acceptance criteria; participate in sprint planning, backlog grooming, and release readiness reviews.
• Run structured discovery with internal customers — quality inspectors, manufacturing engineers, satellite operations staff, analysts, and executives — through interviews, floor-side shadowing, and usage analytics, converting what you learn into prioritized product decisions.
• Define and track product success metrics for each released application (adoption, active usage, task completion, feature engagement, support volume, user satisfaction) and report them to the Chief of AI and Autonomy.
• Own release readiness as a gate: no application ships without release notes, updated user documentation, a known-issues list, and an agreed support plan.
• Write and maintain end-user product documentation for every released application — getting-started guides, workflow walkthroughs, feature references, administrator guides, and versioned release notes.
• Design and maintain in-app guidance: onboarding tours, contextual help, empty states, and error messaging written so users can self-correct without filing a ticket.
• Build and maintain the self-help troubleshooting library — a catalog of common errors, their causes, and step-by-step resolution paths mapped to actual error codes and failure modes, including AI-specific ones such as low-confidence predictions, model timeouts, and degraded offline sync.
• Partner with engineering to ensure errors are instrumented, human-readable, and traceable, turning raw exceptions and API failures into diagnosable user-facing messages with documented remediation steps.
• Co-own the support model with Corporate IT: define tiering and escalation paths, write the IT-facing runbooks and triage scripts used at first contact, and train the help desk on each application before it launches.
• Serve as the escalation point for internal customer issues IT cannot resolve — coordinating with engineering on root cause, workarounds, and permanent fixes, and closing the loop with the affected users.
• Support the Chief of AI and Autonomy in building and executing the transition roadmap as the organization matures its operational support model for AI applications — tracking phases, milestones, and readiness criteria, and flagging risks to the handoff of operational ownership from engineering to steady-state operations.
• Partner with factory NOC staff to define how each released AI application is monitored and supported in production: service catalog entries, health dashboards, alert thresholds and routing, on-call rotations, and escalation paths.
• Help coordinate across the internal AI research lab, data center operations, engineering, and Corporate IT to document clear ownership (RACI) for applications, models, and the compute and network infrastructure they run on, so nothing falls between teams during the transition.
• Write NOC-facing runbooks, standard operating procedures, and incident playbooks for every application; prepare and facilitate transition readiness reviews and tabletop exercises before each responsibility moves.
• Help put incident, problem, and change management practices in place with the NOC — post-incident reviews, change calendars, and planned-maintenance communications to internal customers — so users experience the transition as continuity, not disruption.
• Analyze support tickets and self-help usage to identify recurring failure patterns, feeding documentation updates, UX fixes, and backlog items that measurably reduce ticket volume over time.
• Establish and report service metrics for the portfolio: first-contact resolution, time-to-resolution, self-service deflection, documentation coverage, and user satisfaction.
• Own the documentation system itself — information architecture, style guide, templates, review cadence, and version control — so content stays accurate as applications evolve release over release.
• Run enablement for every new release: launch communications, training sessions, office hours, and champion networks inside each internal customer organization.
• Support partner-facing AI features with externally appropriate documentation, onboarding material, and support workflows in coordination with the teams serving telecom partners. 

Qualifications 

Education:

Bachelor's degree in a technical, business, or communications field, or equivalent experience.

Experience:

• 3-5 years of product management experience, including at least 1 year owning software already in production and used daily — internal platform, enterprise SaaS, or B2B product.
• Demonstrated ownership of product documentation: personally written user guides, release notes, and help content, not only reviewed what others wrote.
• Experience building or running a self-service support model — knowledge base, troubleshooting content, tiering and escalation, and the metrics that prove it is working.
• Working fluency with AI/ML-powered products: able to discuss model behavior, inference, latency, confidence scores, and failure modes credibly with ML engineers, and explain them plainly to a non-technical user.
• Experience partnering with IT or help desk organizations on application support, onboarding, access, and permissions workflows.
• Experience contributing to an operational transition — helping move applications or services from a build team into a NOC, SRE, or managed-operations function — including tracking milestones and readiness criteria against a roadmap.
• Strong analytical skills: comfortable with product analytics and support data, and with SQL or an equivalent for pulling your own numbers.
• Experience working in an agile engineering environment with a mix of remote and on-site teams. 

Preferred Qualifications:

• Experience documenting or supporting applications with conversational AI, LLM/RAG, or computer-vision features.
• Background in technical writing, developer documentation, or docs-as-code workflows (Markdown, Git, static site generators).
• Experience with knowledge base and service platforms (Confluence, Notion, Zendesk, Jira Service Management, ServiceNow) and in-app guidance tools (Pendo, Appcues, WalkMe).
• Familiarity with product analytics and instrumentation planning (Amplitude, Mixpanel, PostHog).
• Familiarity with NOC and IT service management practices (ITIL incident, problem, and change management) and observability platforms (Grafana, Prometheus, Datadog).
• Experience with factory or operational-technology (OT) networks, data center operations, or GPU compute environments supporting AI workloads.
• Experience supporting users in manufacturing, operations, or field environments where downtime carries real cost.
• Experience turning internally built tools into genuine products adopted across an organization.
• Experience with partner-facing or multi-tenant products, including white-label documentation and support requirements.
• Familiarity with API and developer documentation for internal platform consumers.
• Background in aerospace, defense, satellite, or telecommunications. 

Soft Skills:

• Exceptional written communication — clear, concise, structured writing is the core of this role.
• Strong cross-functional collaboration skills, partnering effectively with engineering, IT, and internal customer teams.
• User-obsessed mindset, measuring success by adoption, time-to-resolution, and how rarely people need to ask for help. 

Technology Stack:

• Knowledge base and service management platforms (Confluence, Notion, Zendesk, Jira Service Management, ServiceNow).
• In-app guidance tools (Pendo, Appcues, WalkMe).
• Product analytics and instrumentation tools (Amplitude, Mixpanel, PostHog).
• SQL or equivalent for product analytics and support data.
• Observability platforms (Grafana, Prometheus, Datadog).
• Docs-as-code tooling (Markdown, Git, static site generators). 

Physical Requirements 

• Ability to lift up to 25 lbs.
• Ability to use a computer for extended periods.
• Ability to work in a standard office environment, with occasional visits to manufacturing floor and operations environments.
• Ability to travel occasionally to Midland, TX and Lanham, MD to work alongside users on the manufacturing floor and in operations.

This job description may not be inclusive to the duties and responsibilities listed. Additional tasks may be assigned to the employee from time to time or the scope of the job may change as needed by business demands.