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About This Role
Overview:
We are seeking a highly motivated Product Owner to join our AI\-first product development organization. This hybrid role combines product ownership, business analysis, and AI\-enabled product leadership. You will work closely with stakeholders, engineers, designers, and AI systems to rapidly translate business needs into high\-impact business outcomes.
The ideal candidate has at least 3 years of product delivery within cross\-functional product teams, a proven track record of leveraging AI to improve product discovery, delivery, and decision\-making, and a strong desire to continuously increase productivity through modern AI tools and workflows.
This role is designed for someone who embraces an AI\-first mindset and is excited about using emerging technologies to deliver exceptional results faster, with higher quality and greater customer impact. The successful candidate actively seeks opportunities to use AI to eliminate manual effort, accelerate decision\-making, improve quality, and increase team effectiveness. They can leverage AI to deliver results that would traditionally require significantly larger teams or longer timelines, while maintaining a high standard of quality, collaboration, and customer focus. The salary range for this role is $130,000 to $150,000; however, Lakeview considers several factors when extending an offer, including but not limited to, the role and associated responsibilities, a candidate’s work experience. The role can be 100% remote.
Responsibilities:
Product Ownership:* Own and manage product backlogs, priorities, and delivery outcomes.
- Facilitate sprint planning, backlog refinement, and stakeholder alignment.
- Act as the primary liaison between business stakeholders and delivery teams.
- Ensure delivered solutions align with strategic objectives and customer needs.
- Leverage AI tools to accelerate product discovery, requirements generation, documentation, analysis, and decision\-making.
- Utilize AI\-assisted workflows to improve team velocity, communication, and product quality.
- Champion AI\-first practices across the product lifecycle.
- Continuously identify opportunities to increase productivity through AI\-enabled automation and augmentation.
- Operate, monitor, and tune the production AI agents (e.g., Cost/TCO, UAT, Runbook \& Evidence) across Azure DevOps and Atlassian/Jira via MCP and APIs \- owning the delivery path, not the architecture.
- Apply the PM\-authored quality rubrics at each gate to review and elevate AI\-drafted artifacts before they pass, especially BRDs, TCO, and CBA outputs, where AI output is plausible but can be subtly wrong.
- Catch and correct weak or generic AI output against the defined standard so delivered artifacts read as expert work, not raw AI drafts.
Qualifications:
- 3\+ years of experience as a Product Owner, Product Analyst, Business Analyst, Product Manager, or similar role within a cross\-functional software development team.
- Demonstrated success delivering digital products and features from concept through release.
- Proven ability to work effectively in Agile/Scrum environments.
- Strong analytical, problem\-solving, and prioritization skills.
- Experience writing user stories, acceptance criteria, process flows, and requirements documentation.
- Excellent communication and stakeholder management skills.
- Demonstrated experience leveraging AI tools to improve productivity, product outcomes, or team effectiveness.
- Ability to work independently while driving alignment across diverse teams.
- Hands\-on technical: able to operate, integrate, and extend AI agents and automation across Azure DevOps, Atlassian/Jira, and cloud APIs via MCP, an operating\-and\-building role, not solely backlog management.
- Domain fluency and judgment: enough understanding of the Mortgage business and the product artifacts to recognize weak or incorrect AI output and correct it against a rubric.
Certifications, Licenses, and/or Registration
N/APhysical Demands and Work Environment
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to sit and use hands to handle, touch or feel objects, tools, or controls. The employee frequently is required to talk and hear. The noise level in the work environment is usually moderate. The employee is occasionally required to stand; walk; reach with hands and arms. The employee is rarely required to stoop, kneel, crouch, or crawl. The employee must regularly lift and/or move up to 10 pounds. Specific vision abilities required by this job include close vision, color vision, and the ability to adjust focus.EEOC
Lakeview is an Equal Employment Opportunity employer. All aspects of consideration for employment and employment with the Company are governed on the basis of merit, competence and qualifications without regard to race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, or any other category protected by federal, state, or local law.
Salary Context
This $130K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Lakeview Loan Servicing, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($140K) sits 35% below the category median. Disclosed range: $130K to $150K.
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.
Lakeview Loan Servicing AI Hiring
Lakeview Loan Servicing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Coral Gables, FL, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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).
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 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.
Frequently Asked Questions
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