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About This Role
Product Owner\- Enterprise Data \& AI
Job ID\#: 26\-2281
Location: Herndon, VA/ Remote
Who We Are:
Since our inception back in 2006, Navitas has grown to be an industry leader in the digital transformation space, and we’ve served as trusted advisors supporting our client base within the commercial, federal, and state and local markets.
What We Do:
At our very core, we’re a group of problem solvers providing our award\-winning technology solutions to drive digital acceleration for our customers! With proven solutions, award\-winning technologies, and a team of expert problem solvers, Navitas has consistently empowered customers to use technology as a competitive advantage and deliver cutting\-edge transformative solutions.
What You’ll Do:
The Product Owner is a member of our Enterprise Data \& AI team. Product Owners work daily with teams of business stakeholders, application developers, QA testers, and Scrum Masters to deliver business value through innovative software solutions. The Product Owner is the primary product specialist for the development scrum team, manages the team’s backlog, participates actively in all phases of development, and is a key stakeholder in product related decisions and release planning.
Responsibilities will include but are not limited to:* Product Vision \& Strategy
- Define and maintain the product backlog for the Data \& AI platform, ensuring alignment with the university’s long\-term strategic plan and near\-term priorities.
- Translate institutional goals (student success, research advancement, operational efficiency) into actionable product objectives.
- Stakeholder Engagement
- Collaborate with faculty, researchers, administrators, and IT leaders to gather requirements and identify high\-value data and AI use cases.
- Serve as the primary liaison between business stakeholders and the technical development team.
- Backlog \& Prioritization
- Create, refine, and prioritize the product backlog based on business value, compliance requirements, and technical feasibility.
- Define acceptance criteria and ensure clarity for data, AI, and integration\-related user stories.
- Delivery \& Execution
- Collaborate with data engineers, AI/ML specialists, developers, and architects to deliver platform features and enhancements.
- Support Agile ceremonies (sprint planning, standups, reviews, retrospectives) and ensure the team has a clear understanding of the overall vision and business value of priorities.
- Governance \& Data Stewardship
- Partner with institutional data governance groups to enforce data security, compliance, and stewardship and quality standards and processes.
- Ensure AI applications comply with ethical and responsible AI guidelines in higher education.
- Analytics \& AI Enablement
- Guide the development of AI/BI apps, BI apps, self\-service analytics, and AI\-powered insights for faculty, staff, and leadership.
- Identify opportunities to embed AI/ML models (e.g., student retention predictions, enrollment forecasting, research analytics).
- Change Management \& Adoption
- Communicate platform benefits, features, and updates to stakeholders.
- Drive adoption through demonstrations, training, and user enablement activities.
- Help guide value creation, realization, and measurement through Data \& AI products
- Continuous Improvement
- Monitor product performance, collect feedback, and adjust priorities based on evolving institutional needs.
- Stay current on data \& AI/ML trends and recommend platform innovations.
What You’ll Need:* Bachelor’s degree in Information Science, Information Systems, Computer Science, Data Science, Business Administration, or related field (Master’s preferred).
- 5\+ years of experience in product ownership, business analysis, or project management, ideally in data, analytics, or AI\-focused environments.
- Experience in higher education, research, or public sector environments strongly preferred.
- Proven track record of helping deliver enterprise grade data \& AI platforms and products within SAFe Agile environments.
Set yourself apart:* Strong understanding of data \& AI platforms, data lakes/warehouses, ETL pipelines, APIs, and cloud environments (e.g., Databricks, AWS, Azure, GCP, Salesforce Data Cloud).
- Working knowledge of Data Governance (e.g. Purview) and Master Data Management (e.g. Profisee) tools.
- Familiarity with AI/ML concepts, predictive analytics, and responsible AI frameworks.
- Working knowledge of BI/analytics tools (e.g., Tableau, Power BI, Salesforce CRM Analytics, Looker).
- Experience with data governance, privacy, and compliance (FERPA, HIPAA, GDPR, etc.).
- Strong product management skills: backlog prioritization, user story creation, acceptance criteria.
- Excellent stakeholder engagement and communication skills—able to translate technical concepts into business value.
- Strategic thinker with ability to align platform initiatives to institutional goals (student success, research excellence, operational efficiency).
- Skilled in Agile/Scrum methodologies, Jira/Azure DevOps or similar tools.
- Strong problem\-solving, analytical, and decision\-making skills.
- Ability to drive adoption and change management in diverse academic and administrative communities.
- Certified Scrum Product Owner (CSPO) or SAFe Product Owner/Product Manager.
- Data or cloud certifications (e.g., Databricks, Azure Data Fundamentals, Azure Purview, Azure ADO, Profisee).
- AI/ML or analytics certifications (e.g., Databricks MLFlow or equivalent).
*Equal Employer/Veterans/Disabled*
*Navitas Business Consulting is an affirmative action and equal opportunity employer. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Navitas Human Resources.*
*Navitas is an equal opportunity employer. We provide employment and opportunities for advancement, compensation, training, and growth according to individual merit, without regard to race, color, religion, sex (including pregnancy), national origin, sexual orientation, gender identity or expression, marital status, age, genetic information, disability, veteran\-status veteran or military status, or any other characteristic protected under applicable Federal, state, or local law. Our goal is for each staff member to have the opportunity to grow to the limits of their abilities and to achieve personal and organizational objectives. We will support positive programs for equal treatment of all staff and full utilization of all qualified employees at all levels within Navitas.*
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Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Navitas Business Consulting, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 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.
Navitas Business Consulting AI Hiring
Navitas Business Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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.
Frequently Asked Questions
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