Interested in this AI/ML Engineer role at Libra Solutions?
Apply Now →About This Role
When life gets hard, we make it easier! Libra Solutions helps overcome the burdens created by slow\-moving legal processes. Combining technical innovation and financial strength, we help speed cumbersome workflows and ease financial barriers for our customers. And our companies are leaders in their industries! Oasis Financial is the largest and most recognized national brand in consumer legal funding. Oasis helps consumers awaiting legal settlements to move forward with their lives. MoveDocs is a personal injury solutions platform that integrates and streamlines medical, financial, and professional services for personal injury cases. Our mission is to improve outcomes for plaintiffs, accelerate settlements for attorneys, and ensure timely payment for providers. We are proud of our mission and passionate about applying technology to the challenge of making healthcare more accessible. We also are the leading inheritance funding provider through Probate Advance, helping heirs access their inheritance immediately, without the lengthy process of probate.
Together, under the Libra Solutions banner, we have relationships with over 40,000 attorneys and over 7,000 healthcare providers nationwide, which gives us an amazing platform to service our customers.
Job Description:
The Technical Product Owner is responsible for the full requirements and delivery lifecycle within an assigned engineering pod. The role requires genuine technical depth combined with the product ownership skills to lead stakeholder discovery, translate business complexity into engineering\-ready specifications, and own sprint execution through all Agile ceremonies.
AI\-assisted tooling is central to how this role works. The TPO directs AI workflows to generate and accelerate requirements artifacts across the full product lifecycle — user stories, acceptance criteria, process flows, integration specifications — and applies technical judgment and domain expertise to validate that AI\-generated output is accurate, complete, and aligns to business requirements.
This position reports to the VP/Director, Engineering and can be based in any of our office locations in Denver, CO, Huntersville, NC, Rosemont, IL, or Las Vegas, NV. We welcome strong remote candidates, with occasional travel to Las Vegas as needed.
Position Responsibilities:
- Lead discovery for assigned initiatives; work directly with business stakeholders and subject matter experts to define problem scope, identify root causes, assess solution options, and establish requirements boundaries prior to formal authorship
- Investigate existing system behavior through direct code review, database query execution, and API interaction to establish accurate implementation baselines that inform solution design and requirements precision
- Translate business and operational requirements into technically precise epics, features, user stories, acceptance criteria, data flow diagrams, process flows, and integration specifications
- Direct AI\-assisted tools to generate and accelerate requirements artifacts; validate AI output for technical accuracy, business\-rule fidelity, completeness, and edge case coverage
- Maintain the pod's domain knowledge base — business rules, system documentation, integration patterns, and domain glossaries — structured for accurate consumption by AI agents and engineering teams
- Own and maintain the pod's product backlog, ensuring work is continuously refined, sequenced, and engineering\-ready across new features, enhancements, and technical sustainability work
- Partner with Product Management to translate roadmap direction into engineering\-ready deliverables and assess technical feasibility against business priorities
- Partner with senior engineers and architecture teams on complex integration decisions, cross\-system dependencies, and solution designs that require joint technical and requirements judgment
- Lead all pod ceremonies — refinement, sprint planning, sprint review, and retrospective
- Accept user story delivery through functional validation, acceptance criteria verification, and direct review of pull requests and implementation code; maintain and enforce a consistent definition of done that encompasses both behavioral and implementation standards
Requirements Desired Skills and Experience:
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field, or equivalent hands\-on software development experience
- 3\+ years of experience in technical product ownership, solutions analysis, or software engineering in an Agile software environment
- Working knowledge of software development sufficient to read and reason about application code, evaluate architectural approaches, and assess technical feasibility independently
- Strong knowledge of REST APIs, relational database design, data modeling, and web application architecture
- T\-SQL or equivalent proficiency for data investigation, integration validation, and requirements verification at the database level
- Demonstrated experience as a primary author of user stories, acceptance criteria, and technical requirements documentation
- Working proficiency with AI\-assisted requirements tools, including prompt design, output evaluation, and domain knowledge base management
- Proficiency in Jira and Confluence; experience with Draw.io, Lucidchart or similar diagramming tools
- Strong stakeholder engagement; strong written and verbal communication skills across technical and non\-technical audiences
- Detail\-oriented, collaborative, and self\-directed; able to communicate technical concepts clearly across discipline
- Creative problem\-solving and troubleshooting skills
- Must be authorized to work in the U.S.
Preferred Technical Familiarity:
- Application development experience with C\#, .NET, and Entity Framework
- Front\-end framework experience with React or Angular
- Microsoft SQL Server for data analysis and query execution
- Source control workflows using Git and Bitbucket
- API testing and interaction using Postman or Swagger
Benefits
We believe taking great care of our customers starts with taking great care of our people. That’s why we offer competitive compensation and a comprehensive benefits package, including a choice of multiple medical plans, dental, vision, and life insurance, a 401(k) with generous company match, flexible spending accounts for medical and dependent expenses, and time off to recharge.
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 Libra Solutions, 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 in Demand for This Role
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.
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.
Libra Solutions AI Hiring
Libra Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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