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About This Role
About ARSIEM Corporation
At ARSIEM Corporation we are committed to fostering a proven and trusted partnership with our government clients. We provide support to multiple agencies across the United States Government. ARSIEM has an experienced workforce of qualified professionals committed to providing the best possible support.
As demand increases, ARSIEM continues to provide reliable and cutting\-edge technical solutions at the best value to our clients. That means a career packed with opportunities to grow and the ability to have an impact on every client you work with.
ARSIEM is looking for an amazingly talented AI Product Manager to join our team! In this role you will get to provide strategic and operational guidance to teams developing AI\-enabled solutions. This role is suited for an experienced product\-oriented professional who can help shape priorities, align stakeholders, drive disciplined execution, and keep technical efforts focused on mission value. This individual does not need to be a software engineer but should possess strong technical fluency and enough AI literacy to engage credibly with engineers, data scientists, platform teams, and government leadership. The ideal candidate understands how to translate mission needs into executable plans, keep complex efforts on track, and identify when programs are drifting from useful outcomes. Above all, we want someone with authentic curiosity and passion—someone who actively studies the AI landscape, learns continuously, and brings informed perspective to a fast\-changing field.
This position will support one of our Government clients in Columbia, Maryland.
### Responsibilities
- Provide SETA advisory support to government leadership and technical teams on AI product strategy, planning, and execution.
- Help define and refine AI use cases, product goals, success criteria, priorities, and delivery roadmaps.
- Guide teams in aligning AI solution development with mission outcomes, user needs, operational constraints, and program objectives.
- Monitor effort execution and help keep teams on track with milestones, dependencies, risks, and decision points.
- Support requirements development, backlog shaping, capability prioritization, and stakeholder coordination.
- Translate between technical teams and non\-technical leadership, ensuring shared understanding of what is being built, why it matters, and what risks exist.
- Advise on AI adoption challenges, including scope control, change
- management, user trust, operational fit, and realistic implementation sequencing.
- Review proposed AI solutions for feasibility, mission relevance, and alignment with available resources and timelines.
- Support briefings, planning artifacts, concept papers, product documentation, and executive communications.
- Help identify when teams need course correction, sharper problem framing, better metrics, or more disciplined execution.
### Minimum Qualifications
- Min 18 years with Bachelors, 15 years with Master's degree in a relevant field such as Business, Engineering, Computer Science, Information Systems, Operations, or related discipline.
- Experience in product management, technical program support, capability delivery, or requirements management in a government, defense, or complex enterprise environment.
- Demonstrated ability to work with technical teams building software, data, AI, or digital solutions.
- Strong understanding of product lifecycle concepts, roadmap planning, prioritization, stakeholder management, and execution tracking.
- Familiarity with AI concepts, terminology, current capabilities, and practical implementation challenges.
- Ability to advise leadership while also engaging productively with engineers and solution teams.
- Excellent written, verbal, and briefing skills.
### Preferred Qualifications
- Experience supporting AI, data, analytics, or software modernization efforts.
- Familiarity with Agile, SAFe, DevSecOps, or iterative capability delivery models.
- Ability to understand technical topics such as LLMs, AI tools, cloud platforms, agentic workflows, evaluation, and data dependencies without needing to be a hands\-on developer.
- Experience in defense, cyber, or operational mission environments.
- Background in technology strategy, innovation support, requirements development, or portfolio management.
Clearance Requirement: This position requires an active TS/SCI with a polygraph. You must be a U.S. citizen for consideration.
Candidate Referral: Do you know someone who would be GREAT at this role? If you do, ARSIEM has a way for you to earn a bonus through our referral program for persons presenting NEW (not in our resume database) candidates who are successfully placed on one of our projects. The bonus for this position is $3,500, and the referrer is eligible to receive the sum for any applicant we place within 12 months of referral. The bonus is paid after the referred employee reaches 6 months of employment.
ARSIEM is proud to be an Equal Opportunity and Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, age, or any other federally protected class.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Arsiem, 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 $213,800 based on 583 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Arsiem AI Hiring
Arsiem has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Columbia, MD, US, Fort Meade, MD, US. Compensation range: $185K - $185K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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