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About This Role
Product Analyst, AI Solutions
The Product Analyst, AI Solutions is responsible for shaping, optimizing, and maintaining advanced AI-driven features and user experiences within our product suite. This role sits at the intersection of business analysis, AI tuning, and user experience—combining expertise in software requirements gathering, prompt engineering, and AI model evaluation. The Analyst is heavily focused on refining how AI systems behave in real-world scenarios, ensuring outputs are accurate, trustworthy, and closely aligned with client needs.
You partner with AI Engineers, Product Managers, UX/UI Designers, QA, and Subject Matter Experts (SMEs) to extract user requirements, translate those into actionable User Stories and evaluation metrics, and continuously tune AI systems for best-in-class performance and usability.
This role is pivotal in ensuring our AI solutions provide maximum business value, aligning advanced technology with end-user needs while driving continuous improvement and reliability. The Analyst produces high-quality deliverables using systematic processes, with impact across multiple projects, deadlines, and teams.
Principal Duties
AI Tuning & Output Quality
- Design, evaluate, and refine prompts, templates, and conversation flows for reliability, cost effectiveness, and user alignment.
- Maintain prompt libraries, track changes, and manage versioning.
- Systematically test AI model outputs for accuracy, consistency, compliance, and tone, applying rerankers, guardrails, and validation layers to ensure business rules are met.
- Collaborate with engineering and QA to promptly integrate feedback and resolve model drift or new scenarios.
Requirements Gathering & Documentation
- Proactively gather requirements of moderate scope and complexity from diverse sources (clients, market analysis, SMEs) to drive product development and AI enhancements.
- Document software and system requirements, including user stories, program functions, test cases, and steps required for new features or modifications.
- Ensure all documentation supports future maintainability and growth of AI-enabled features.
Evaluation, Metrics, & Continuous Improvement
- Establish systematic evaluation frameworks for AI model outputs, leveraging quantitative and qualitative benchmarks.
- Run experiments, A/B tests, and structured evaluations to compare tuning strategies and track performance metrics (accuracy, coverage, hallucination rates, latency).
- Translate business and user feedback into actionable improvements, owning continuous iteration for released solutions.
Collaboration & UX Alignment
- Act as a bridge between technical and non-technical stakeholders, ensuring clear, effective communication and alignment.
- Work closely with product, development, and UX/UI teams to ensure AI-driven features fit seamlessly into business workflows, are intuitive and user-friendly, and meet client needs.
- Help shape the overall user experience for AI interactions, promoting explainability and transparency.
Education, Experience and Special Skills
- Bachelor’s degree in Computer Science, Management Information Science, Business, or related field OR equivalent work experience.
- 1+ years’ experience in software, AI/ML, or related product analysis roles.
- Experience with AI/ML applications (LLMs, NLP, machine learning pipelines).
- Strong understanding of prompt engineering and generative AI interaction design.
- Basic knowledge of data modeling, relational database concepts, and SQL.
- Familiarity with model evaluation techniques and performance metrics.
- Proficiency with Python or JavaScript for prototyping and evaluation.
- Experience with Agile SCRUM development processes.
- Excellent planning, organization, and interpersonal communication skills.
- Ability to work independently and collaboratively across functional groups.
- Experience in ERP systems, enterprise software, or complex business domains.
- Familiarity with RAG (retrieval-augmented generation), embeddings, MLOps, and responsible AI practices.
- Experience with fine-tuning methods (LoRA, QLoRA, PEFT), running structured evaluation series, or human-in-the-loop systems.
Salary Context
This $78K-$93K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Tyler Technologies, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($85K) sits 60% below the category median. Disclosed range: $78K to $93K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Tyler Technologies AI Hiring
Tyler Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Yarmouth, ME, US. Compensation range: $93K - $93K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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