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About This Role
Section 1: Position Summary
The Senior AI Product Owner plays a key role in guiding, expanding, and refining AI\-powered product capabilities in challenging, high\-impact sectors. This position is responsible for defining, planning, and delivering AI products and features, starting from initial discovery and use–case validation right through to backlog management and achieving clear business results. Strong leadership and influence, effective communication with diverse groups, relationship\-building, and collaboration with both business and technology teams in a fast\-paced setting are essential requirements for this role
Section 2: Job Functions, Essential Duties and Responsibilities
- Strategic Planning and Roadmap Definition:
- Represent domain/focus area to leadership, stakeholders, partner teams, and senior leadership
- Engage with stakeholders/end users to identify key business problems and represent the voice of the customer on complex and challenging product initiatives
- Lead shaping and planning efforts, coordinating across teams as needed to optimize the execution strategy
- Participate in data analysis, user research, design sessions, and usability testing
- Define, maintain, and socialize actionable product roadmaps, highlighting key capabilities required to achieve business outcomes
- Partner with capability leaders/product managers to define success metrics and demonstrate impact
Section 3, Requirements:
Experience (required)
- *E**xperience* *owning and delivering AI**‑enabled product capabilities*
- A minimum of 5\-10 years of agile product development experience
- 5\+ working within scrum teams in the Product Owner role with demonstrated success delivering business results
- Financial Services/Retirement Services industry Experience
Personal Qualities
- Strong communicator
- Committed, enthusiastic, \& energetic
- Analytical and effective at decision\-making
- Decisive and able to operate independently Results\-oriented with a commitment to excellence
- Proactive, flexible, and resourceful
Skills/Capabilities
- Expertise in planning and execution of product roadmaps with knowledge of agile methodologies and practices
- Excellent communication skills with the ability to adjust communication for varying audiences including senior leadership
- Highly organized with the ability to effectively prioritize deliverables to achieve business outcomes
- Highly motivated with a desire to lead and influence Ability to work independently, problem solve, and make thoughtful recommendations on the path forward
- Strong leadership skills with the desire to mentor others and provide guidance and support to more junior team members
Professional Development:
- Define goals for professional development and work to continually enhance knowledge of domain, systems, products, and industry
- Partner with Product Development leadership to establish best practices for Product Owners and Business Analysts
- Expand knowledge of agile/scrum, apply best practices, and drive continuous improvement
Leadership and Mentoring:
- Provide mentoring and leadership to develop less experienced Product Owners and other team members partnering with leadership to grow talent within the organization
- Participate in training opportunities and monthly Product Owner Community of Practice (CoP)
- Use and continually develop servant leadership skills to foster inclusiveness, communication, and collaboration
Collaboration and Communication:
- Effectively articulate the product vision and business outcomes to ensure alignment to the “why” within scrum teams and for varying audiences across the firm
- Act as the primary point of contact for updates and feedback related to the domain and supporting scrum team(s)
- Effectively communicate the product narrative, clearly demonstrating your team(s) alignment to and role in achieving the product vision Cultivate the ability to influence, effectively adjusting communication strategies for different audiences
Product Development and Backlog Management:
- Define features and create detailed user stories with actionable acceptance criteria
- Prioritize features in alignment with product vision, goals and strategic objectives, leveraging user data to drive decision\-making
- Drive execution within and across scrum teams, providing guidance and direction to develop innovative and scalable solutions that achieve business outcomes
- Maintain an organized product backlog, capturing evolving requirements and adjusting priorities to maximize value delivered
- Actively participate in team\-level and program\-level agile ceremonies including daily stand\-ups, refinement, sprint reviews/demos, release and sprint planning, and retrospectives
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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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Ascensus, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Ascensus AI Hiring
Ascensus 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 $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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