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About This Role
AI Engineer II (Hybrid) YOU MUST CURRENTLY BE IN OMAHA AREA!
Requisition Job Title: AI Engineer II
Division: Business Technology
Department: Data \& Analytics
Location: Home Office (Hybrid – Omaha, NE preferred)
Role Overview
How This Role Fits into WoodmenLife
This role is part of a newly formed AI team within the Data \& Analytics function, currently consisting of an AI Engineer Lead (III), Data Scientist I, and an AI Engineer intern. The team works closely with the Data Analytics teams and stakeholders across Business Technology to deliver enterprise AI capabilities.
The AI Engineer II plays a critical role in building and deploying scalable, production\-grade AI solutions that support core business functions. This position helps drive WoodmenLife’s transformation into an AI\-first organization, modernizing processes across underwriting, claims, customer experience, and risk assessment.
Why You’ll Love This Role
WoodmenLife is seeking an AI Engineer II to help bring AI solutions from concept to reality across the enterprise. In this role, you’ll build systems that go beyond experimentation—delivering real, measurable business impact.
This position is ideal for someone passionate about applying AI in production environments, solving meaningful business problems, and working across a modern cloud\-based ecosystem. You’ll gain exposure to end\-to\-end AI delivery, collaborate with cross\-functional teams, and play a foundational role in scaling AI capabilities across the organization.
WoodmenLife offers a supportive, collaborative environment that encourages growth, innovation, and continuous learning.
This is the Life (WoodmenLife)
As a not\-for\-profit life insurance company, WoodmenLife is devoted to protecting the financial futures of families and making a difference in hometowns across America. With over 135 years of success, our commitment extends beyond our members—we invest in our associates.
We provide an environment that encourages professional growth, creativity, and innovation, while supporting meaningful work that directly impacts our members and communities.
What You’ll Do
Key Functions
Design and develop AI/ML solutions aligned with business objectives
Build and deploy RAG augmented CoPilots into enterprise production environments
Develop data pipelines and infrastructure to support AI workloads
Monitor model performance and implement continuous improvements
Ensure AI solutions meet security, compliance, and governance standards
Day\-to\-Day Responsibilities
Design and build AI/ML solutions for various business units
Design and integrate Agentic processes into enterprise systems and workflows
Maintain and optimize data pipelines for training and inference
Monitor model accuracy, drift, and performance metrics
Collaborate with cross\-functional teams to deliver scalable solutions
Participate in code reviews, documentation, and best practice development
AI Engineering \& Delivery
Build prototypes, MVPs, and production\-ready AI applications
Develop agentic workflows and intelligent automation solutions
Implement APIs and services for agent integration
Apply AIOps/MLOps practices for deployment, monitoring, and lifecycle management
Collaboration \& Continuous Improvement
Partner with Data Teams, IT, and business stakeholders
Contribute to enterprise AI strategy and adoption
Stay current with emerging AI tools and technologies
Promote responsible AI practices including fairness, explainability, and compliance
Tools \& Technologies
Languages: Python, SQL, C\#
Frameworks: TensorFlow, PyTorch, ML.NET
Cloud Platforms: Azure
Tools: Power Automate, AI Foundry, Power Platform
Data Platforms: Snowflake (preferred), SQL Server, Fabric
Projects \& Initiatives
Advancing enterprise AI/ML capabilities and platforms
Building intelligent automation and agent\-based workflows
Strengthening the organization’s data and AI foundation
Driving responsible AI governance and compliance practices
Expanding AI adoption across business functions
Qualifications
Minimum Qualifications
Bachelor’s degree in Computer Science, Data Science, or related field
3–5 years of experience developing AI/ML solutions in production environments
Strong programming skills in Python, SQL, and C\#
Experience with machine learning, deep learning, or NLP
Experience with cloud platforms and MLOps practices
Strong analytical, problem\-solving, and communication skills
Ability to manage multiple priorities and meet deadlines
Preferred Qualifications
Experience in insurance or financial services
Familiarity with underwriting, claims, or risk workflows
Experience with Microsoft technology stack and Snowflake
Exposure to responsible AI and governance frameworks
Career Growth
AI Engineers may advance to AI Engineer III, with opportunities to expand into senior engineering, architecture, or AI leadership roles as the organization continues scaling its AI capabilities.
Operational Considerations
This is a hybrid role based in Omaha, NE. Candidates should be able to work on\-site as needed to support team collaboration and strategic initiatives.
Leadership Insight
Leadership style is people\-centered, focusing on understanding individual strengths, empowering ownership, and fostering a collaborative environment where team members can grow and succeed.
WoodmenLife offers a competitive compensation and benefits package. Learn more:
https://www.woodmenlife.org/careers/home\-office/benefits/. Employment is contingent upon successful completion of background checks, which may include criminal, credit, fingerprinting (where required), drug screening, and reference checks. WoodmenLife is committed to creating an inclusive environment that values diverse perspectives and provides equal opportunities for growth, leadership, and service.
Salary Context
This $107K-$150K range is in the lower quartile 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 WoodmenLife, 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 ($128K) sits 40% below the category median. Disclosed range: $107K 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.
WoodmenLife AI Hiring
WoodmenLife has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, 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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