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About This Role
Senior AI Product Owner \- R01566192
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About Brillio:
Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world\-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting\-edge digital and design thinking skills with an unwavering dedication to client satisfaction.
Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year. Senior AI Product Owner###### Job requirements
We are seeking a Senior Product Owner with a strong AI and analytics background to work onsite in Santa Ana, CA, leading AI‑driven product initiatives for a strategic client in the construction and built‑environment domain.
This role is highly client‑facing and delivery‑oriented, requiring close collaboration with construction SMEs, business stakeholders, and engineering teams. The ideal candidate brings strong product ownership skills, hands‑on understanding of AI/ML solutions, and nice‑to‑have domain exposure to Division 8 (doors, frames, hardware, glazing systems).
Key Responsibilities
Product Ownership \& Delivery
- Own the product backlog, roadmap, and sprint priorities for AI and analytics initiatives.
- Translate business needs into detailed user stories, acceptance criteria, and product requirements.
- Drive sprint planning, backlog grooming, demos, and release readiness.
- Ensure predictable delivery, quality outcomes, and alignment with client priorities.
AI \& Analytics Enablement
- Lead AI‑enabled use cases including:
- Predictive and operational analytics
- Machine Learning solutions
- Computer Vision (drawings, images, site inspections)
- Document intelligence, OCR, and GenAI / LLM‑based applications
- Collaborate closely with data science and engineering teams on solution design, experimentation, and production deployment.
- Support AI lifecycle governance, performance tracking, and continuous improvement.
Construction Domain Engagement
- Work onsite with client stakeholders across construction operations, estimation, procurement, and project delivery.
- Apply construction domain context to shape meaningful AI and analytics features.
- Leverage Division 8 knowledge (doors, frames, hardware, glazing, access systems) where applicable to inform product decisions.
- Bridge communication between construction SMEs and technical delivery teams.
Client \& Stakeholder Management
- Act as the primary onsite point of contact for product‑related discussions.
- Facilitate workshops, requirement discussions, and solution walkthroughs.
- Provide regular updates on progress, risks, and dependencies to client leadership.
- Coordinate effectively with offshore and near‑shore delivery teams.
Required Qualifications
Experience
- 8–12\+ years of overall experience across product management, analytics, AI/ML, or enterprise technology delivery.
- 4–6\+ years of experience as a Product Owner or Product Manager for data‑ or AI‑driven products.
- Proven experience working onsite with US clients, preferably in consulting or enterprise delivery environments.
- Early‑career hands‑on experience in analytics, data engineering, data science, or software engineering.
Technical Skills
- Strong understanding of:
- AI/ML concepts and solution lifecycles
- Analytics platforms and data ecosystems
- Cloud platforms (GCP, Azure, or AWS)
- APIs, data pipelines, and system integrations
- Ability to translate technical concepts into clear business‑focused outcomes.
Good to Have / Preferred
- Experience in the construction or built‑environment industry.
- Division 8 domain knowledge (doors, frames, hardware, glazing systems).
- Exposure to ConTech platforms, BIM/CAD workflows, or blueprint/document analytics.
- Experience applying Computer Vision or GenAI in industrial or operational settings.
Band C Expectations
- Strong individual contributor with end‑to‑end ownership of product delivery.
- Comfortable working independently in an onsite client environment.
- Hands‑on in backlog management, requirement definition, and stakeholder engagement.
Work Location \& Travel
- This is an onsite role based in Santa Ana, California.
- Presence at the client location is required.
- Occasional travel within the US may be needed based on project demands.
Education
- Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, Analytics, or related field.
- Product management or agile certifications (CSPO, SAFe POPM, etc.) are a plus.
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Equal Employment Opportunity Declaration
Brillio is an equal opportunity employer to all, regardless of age, ancestry, colour, disability (mental and physical), exercising the right to family care and medical leave, gender, gender expression, gender identity, genetic information, marital status, medical condition, military or veteran status, national origin, political affiliation, race, religious creed, sex (includes pregnancy, childbirth, breastfeeding, and related medical conditions), and sexual orientation.
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Salary: 165,000\-170,000 USD per year salary
Salary Context
This $165K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 Brillio LLC, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($167K) sits 8% below the category median. Disclosed range: $165K to $170K.
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.
Brillio LLC AI Hiring
Brillio LLC has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager. Positions span New York, NY, US, Santa Ana, CA, US, Jersey City, NJ, US. Compensation range: $130K - $180K.
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/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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