Interested in this AI/ML Engineer role at Public School Retirement Systems of Missouri?
Apply Now →About This Role
PSRS/PEERS is seeking an innovative, highly skilled, and forward\-thinking AI Innovation Engineer to join our Information \& Technology Team. In this pivotal technical role, you will design and deliver automation solutions, intelligent digital agents, and data\-driven capabilities that accelerate modernization across the organization. Working under the general direction of the Chief Technology Officer (CTO), you will directly shape how PSRS/PEERS leverages emerging technologies to enhance efficiency, strengthen governance, and improve the user experience for staff and members across Missouri’s public education community.
As the AI Innovation Engineer, you will build sustainable digital workflows and AI\-enhanced automations that reduce manual effort, protect institutional knowledge, and improve the reliability of critical processes. You will integrate automation with analytics, ensure strong data governance, collaborate across departments, and provide expertise that supports secure, thoughtful digital transformation. Your work will enable faster project delivery, intuitive user experiences, and long\-term operational resilience throughout the organization.
Salary:
$112,756 \- $140,945 annually.
Pay within this advertised range is commensurate with education, experience, and technical proficiency.
Job Type:
Full\-time with benefits.
Location:
Jefferson City, Missouri.
About PSRS/PEERS
PSRS/PEERS is the largest defined benefit pension system in Missouri, proudly serving more than 300,000 members statewide. We partner with public school districts to provide secure and reliable retirement benefits to Missouri’s educators and education employees—ensuring financial stability through every stage of their careers and beyond.
Our Information Technology Team operates from our newly remodeled and expanded headquarters at 3210 West Truman Boulevard in Jefferson City. The facility features state\-of\-the\-art technology, advanced security infrastructure, ergonomic workspaces, and open collaboration areas designed to support teamwork and innovative problem\-solving. We offer a flexible, employee\-focused work environment with ample on\-site parking and scheduling flexibility within our standard office hours of 7:30 a.m. to 4:30 p.m., Monday through Friday.
Your Role at a Glance
As the AI Innovation Engineer, you will bring structure, reliability, and innovation to the organization’s automation and AI ecosystem. You will design modern digital workflows, build intelligent agents, and establish the standards that ensure secure, governed, and well\-managed automation across departments. This role blends technical development, solution architecture, cross\-functional collaboration, and ongoing optimization.
You will partner closely with Business Systems Analysts, Data Analysts, Project Managers, and end users to identify opportunities for automation and translate those needs into high\-performance digital solutions. You will evaluate emerging technologies, assess their organizational impact, and introduce new approaches to streamline operations. Your work will ensure that automation is not only technically sound, but also sustainable, measurable, secure, and aligned with organizational priorities.
Examples of your impact include:
- Designing scalable automation and AI\-powered digital agents that reduce manual workloads and activate modernization goals.
- Improving organizational agility by accelerating turnaround times for repeatable processes.
- Strengthening governance by embedding data accuracy, security, and auditability into digital workflows.
- Enhancing employee experience through intuitive automations that eliminate friction and streamline work.
- Ensuring that digital solutions are monitored, optimized, and upgraded for long\-term sustainability.
Job Requirements
The ideal candidate for this position will combine hands\-on technical skills with strong analytical reasoning, collaboration abilities, and a deep understanding of automation best practices.
Required Education \& Experience
- Associate degree in Information Technology *and* at least 5 years of IT experience, or
- Bachelor’s degree in Information Technology or a related field *and* a minimum of two (2\) years of IT experience.
- Two or more years of direct IT experience.
- Demonstrated ability to use technology creatively to solve business needs.
Key Technical Knowledge
- Automation development frameworks, programming languages, and workflow tools.
- Data governance, security practices, and system integration techniques.
- Requirements\-gathering, process analysis, and cross\-functional collaboration.
- Emerging technologies supporting automation, analytics, and AI\-driven modernization.
- Solution monitoring, troubleshooting, and long\-term optimization.
Core Skills \& Competencies
- Ability to design, build, and maintain sophisticated automation solutions and AI agents.
- Skill in integrating automation with analytics and data governance standards.
- Clear and effective communication across technical and non\-technical audiences.
- Ability to evaluate new technologies and determine fit for organizational needs.
- Strength in diagnosing issues, interpreting logs/telemetry, and enhancing reliability.
What You’ll Gain
Stepping into the role of AI Innovation Engineer means contributing directly to the modernization and digital evolution of PSRS/PEERS. You will help define how automated processes are built, how digital agents behave, and how the organization incorporates emerging technologies in secure, responsible ways.
You will work collaboratively across departments, influencing how institutional knowledge is preserved, how data\-driven decisions are supported, and how automation enhances mission\-critical functions. Your solutions will help ensure continuity, reduce errors, and allow staff to focus on meaningful, high\-value work—ultimately strengthening the retirement security of Missouri’s educators and education employees.
Why You’ll Love Working at PSRS/PEERS
PSRS/PEERS is committed to supporting every employee as much as they support our mission. We offer:
- A flexible, employee\-friendly work environment
- Opportunities to telecommute
- A robust benefits package including medical, dental, vision, and a Health Savings Account (HSA)
- Membership in a defined benefit pension plan with lifetime benefits after just five years of vesting
- Generous paid time off: three weeks of vacation, three weeks of sick leave, and 12 paid holidays annually
- Tuition assistance, professional development, and leadership development programs
- A strong culture of community through events, volunteer initiatives, and employee\-led charitable efforts
Salary Context
This $112K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Public School Retirement Systems of Missouri, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($126K) sits 42% below the category median. Disclosed range: $112K to $140K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Public School Retirement Systems of Missouri AI Hiring
Public School Retirement Systems of Missouri has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Jefferson City, MO, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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