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About This Role
The MFM IT team has a long history of delivering and maintaining core business systems in a mid\-sized Property \& Casualty insurance environment. As we expand our development team and adopt new AI development tools, we are making a deliberate investment in building this capability correctly from the start. As we begin to build agentic AI capabilities within our enterprise system, we are looking for an experienced engineer with strong knowledge of agentic AI systems to help guide this effort.
The Senior Agentic AI Specialist will work closely with MFM’s Principal Architect, who leads overall platform architecture, and partners with senior developers to design, implement, and improve agent\-based solutions. This role will bring specialized expertise in areas where we are still developing depth, including agent orchestration, retrieval pipelines, LLM evaluation, and prompt design. This is an ongoing platform effort, not a one\-time project. The work will evolve over time as we expand capabilities, integrate new tools, and improve system quality.
Our core systems are built on a Microsoft/.NET stack. As we develop our agentic AI capabilities, we expect to leverage cloud\-based tooling (likely within Azure) where appropriate. We are pragmatic about using the right tools for the problem, while maintaining alignment with our existing platform and operational requirements.
Responsibilities
- Platform Architecture \& Evolution:
- Provide input into AI\-related architecture decisions. Evaluate tools, models, and approaches, and make practical recommendations based on trade\-offs and outcomes.
- Agent Lifecycle Management:
- Design, build, and maintain agents. Define prompts and configurations, support routing decisions, and monitor performance in production.
- RAG Pipeline Engineering:
- Improve retrieval quality by working with chunking strategies, embeddings, search methods, and metadata filtering. Support onboarding of new knowledge sources.
- Quality \& Hallucination Reduction:
- Help reduce incorrect or unsupported outputs through system design, testing, and monitoring. Apply practical approaches across prompts, retrieval, and model behavior.
- Evaluation \& Testing:
- Develop and maintain evaluation approaches to measure output quality, including relevance and accuracy. Support regression testing and identify failure patterns.
- Prompt Engineering:
- Create and maintain prompts for different agents. Diagnose unexpected behavior and develop reusable patterns where appropriate.
- Tool Integration:
- Support integration with internal systems and APIs. Help manage tool access and ensure reliable execution and error handling.
- Observability \& Operations:
- Contribute to logging, tracing, and monitoring. Support analysis of system behavior, cost, and performance in production.
- Governance \& Configuration:
- Work with configuration across agents, prompts, and models. Ensure changes are version\-controlled and reviewed.
- Developer Support:
- Work with other developers to share knowledge, review designs, and establish consistent approaches.
- Research \& Evaluation:
- Stay current with new models and tools. Evaluate them in a structured way and recommend adoption where appropriate.
### Position Requirements
### Education \& Certification
- College degree or Programming Certification (preferred)
- 5\+ years of equivalent work experience
### Required Experience
- Experience building or working with AI/LLM\-based systems in a production or near\-production environment
- Strong understanding of agent\-based patterns (ReAct, tool usage, etc.)
- Experience with retrieval\-based systems (RAG), vector stores, or search pipelines
- Familiarity with evaluating non\-deterministic system outputs
- Experience improving system quality through testing and iteration
- Strong software engineering skills required. Experience with Python, TypeScript, or C\# preferred
- Ability to work across technologies and integrate AI solutions into an existing enterprise environment
### Preferred Experience
- Experience working with multiple LLM providers
- Familiarity with emerging tools and frameworks in the agentic AI space
- Experience integrating with enterprise systems and data sources
- Experience mentoring or supporting other developers
### Personal Attributes
- Strategic thinker with strong analytical and problem\-solving skills
- Technically proficient with broad systems knowledge
- Excellent interpersonal, written, and verbal communication skills
- Self\-directed, collaborative, and highly organized
- Customer\-oriented and able to navigate high\-pressure environments
- Flexible and adaptable in regards to learning and understanding new technologies.
- Exceptional organizational skills and ability to keep track of multiple projects at a time.
- Ability to conduct research into software\-related issues and products.
- Technically proficient.
- Exceptional attention to detail.
- Ability to work both independently and in a team\-oriented, collaborative environment.
### Work Conditions
- Remote work environment following core hours 8AM\-5PM Central Time
- Overtime may be required to meet project deadlines or monitor system processes.
- Sitting for extended periods of time.
- Dexterity of hands and fingers to operate a computer keyboard, mouse, and other devices.
- Minimal travel will be required for the purpose of off\-site software training and team meetings.
- Physically able to participate in virtual and in\-person training sessions, presentations, and meetings.
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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Midwest Family Mutual, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Midwest Family Mutual AI Hiring
Midwest Family Mutual has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Urbandale, IA, US.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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