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About This Role
At Technatomy, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customer’s success. We provide solutions to agencies and entities including the Department of Veterans Affairs, Department of Defense, Defense Logistics Agency, National Institute of Health, and more. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community. Our Mission, Vision, and Values guide the way we do business.
If this sounds like an environment where you can thrive, keep reading!
We are seeking an experienced BPM / UX / AI Workflow Specialist to shape business processes, user interactions, workflow behavior, and governed AI\-enabled reporting concepts for an enterprise Pega\-based modernization effort within the Department of Veterans Affairs. The position is grounded in process and user\-centered design; it does not require the candidate to be a data scientist or model developer. The role works across stakeholders, analysts, architects, developers, testers, and trainers to create practical, accessible, traceable, and configurable workflows.
DUTIES AND RESPONSIBILITIES:
- Facilitate discovery and design sessions to understand current operations, pain points, policy constraints, user roles, and desired outcomes.
- Develop and maintain current\-state and future\-state process models, workflow diagrams, use\-case inputs, user journeys, personas, and role\-based interaction flows.
- Apply BPMN, human\-centered design, and workflow\-simplification techniques across planning, budgeting, execution, acquisition, approvals, reporting, and compliance processes.
- Collaborate with business analysts and Product Owners to translate process and usability findings into user stories, acceptance criteria, prototypes, and backlog priorities.
- Work with Pega architects and developers to ensure proposed workflows can be delivered through configuration\-first, reusable, and maintainable patterns.
- Review navigation, screen flows, content organization, forms, error handling, and in\-application guidance for usability and accessibility.
- Define AI\-assisted reporting and workflow use cases, including template logic, data\-selection needs, review points, explanations, citations, and human\-in\-the\-loop controls.
- Identify opportunities for workflow automation, checklist automation, standardization, and removal of unnecessary handoffs or offline workarounds.
- Support sprint demonstrations, usability reviews, test planning, UAT, and resolution of process\- or experience\-related defects.
- Provide role, workflow, and job\-impact inputs to training, communications, and change\-management activities.
KNOWLEDGE AND SKILLS REQUIRED:
- 5\+ years of experience in business process management, workflow analysis, UX analysis, human\-centered design, business analysis, or process improvement for software delivery.
- Experience documenting current\-state and future\-state processes, user journeys, roles, decisions, exceptions, and system interactions.
- Working knowledge of BPMN 2\.0 or comparable process\-modeling practices and tools.
- Practical understanding of usability, accessibility, role\-based workflows, stakeholder validation, and iterative design.
- Ability to translate complex business processes into clear workflow requirements, prototypes, user stories, and acceptance criteria.
- Working familiarity with AI\-assisted reporting, natural\-language generation, automation, or decision\-support concepts; deep model\-development experience is not required.
- Strong facilitation, analytical, visual\-communication, documentation, and stakeholder\-engagement skills.
- Experience working with Agile product teams in a remote, client\-facing environment.
KNOWLEDGE AND SKILLS DESIRED:
- Experience in Federal, financial, acquisition, health, or another regulated business environment; direct VA experience is optional.
- Exposure to Pega, Appian, Salesforce, or another workflow/case\-management platform.
- Experience with tools such as Visio, Lucidchart, Figma, Miro, or comparable modeling and prototyping tools.
- Working knowledge of Section 508/WCAG, ethical AI, explainability, RPA, or configurable report\-generation concepts.
- BPM, UX, HCD, Agile, Lean Six Sigma, or process\-improvement certification is preferred, but not required.
EDUCATION:
- Bachelor’s degree in Business Administration, Information Systems, Human\-Computer Interaction, Design, Computer Science, Engineering, or a related discipline, or an equivalent combination of education and experience.
CLEARANCE:
- Must be able to obtain and maintain a Public Trust clearance.
WORK LOCATION:
- Remote
As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
This position requires U.S. citizenship or Green card.
This position is contingent upon contract award.
Technatomy Corporation is an Equal Opportunity Employer. It is the policy of Technatomy Corporation to afford equal employment opportunity regardless of race, color, religion, national origin, sex, age, marital status, disability or veteran status, or any other status protected by applicable law.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Technatomy Corporation, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Technatomy Corporation AI Hiring
Technatomy Corporation has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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