Interested in this AI/ML Engineer role at Wespath Benefits and Investments?
Apply Now →Skills & Technologies
About This Role
Senior AI/ML Engineer
Location:
1901 Chestnut Ave
Glenview, Illinois, 60025
United States
Category:
Information Technology
Date Needed by:
11/5/2026
Primary Duties and Responsibilities:
You bring the motivation, teamwork, and integrity and we will provide the collaborative culture, professional development, and opportunity to build a meaningful career at Wespath!
Wespath is an Equal Opportunity Employer that is committed to a diverse and inclusive company culture and does not discriminate against applicants and employees because of disability, sex, race, gender identity, sexual orientation, religion, national origin, age, veteran status, or any other protected status under the law.
Wespath’s Information Technology team has an opportunity for a Senior AI/ML Engineer. This role is a hands\-on software developer responsible for designing, building, deploying, and supporting secure AI\-enabled solutions that improve business and IT productivity. This role develops AI agents, copilots, automations, integrations, and reusable components using approved enterprise data, documentation, APIs, Microsoft AI platforms, AWS AI services, and cloud\-native deployment patterns. The position partners with Product Management, business stakeholders, IT teams, and governance partners to turn defined opportunities into reliable, scalable, and production\-ready AI solutions that reduce manual effort, improve knowledge discovery, accelerate software delivery, enhance service quality, and increase operational efficiency.
More specifically, as a Senior AI/ML Engineer, you will have the following key responsibilities:
- Design, build, test, deploy, and support AI\-enabled applications, agents, copilots, automations, and integrations.
- Evaluate market and software trends to recommend useful AI solutions to IT leadership.
- Recommend software platforms and solution approaches that align with business and technology needs.
- Help shape organization\-wide AI strategy and data practices for management, governance, curation, and AI readiness.
- Convert defined business and IT use cases into practical, production\-ready AI solutions that deliver measurable business value through automation, improved decision support, increased productivity, and operational efficiency.
- Partner with stakeholders to define success criteria and measure business impact of deployed AI solutions.
- Develop agents that securely use approved enterprise content, data sources, APIs, and workflows to answer questions or complete tasks.
- Prepare enterprise documents and data for AI use through metadata, indexing, retrieval design, validation, and content quality improvements.
- Build reusable prompts, skills, connectors, templates, and technical patterns that accelerate future AI solution delivery.
- Integrate AI solutions with enterprise systems, collaboration tools, identity services, approved data platforms, and cloud\-native services across Azure, AWS, or similar environments.
- Apply approved standards for security, privacy, PHI/PII protection, access control, monitoring, and responsible AI use.
- Monitor, troubleshoot, improve, and document deployed AI solutions so they remain reliable, supportable, and aligned with user needs.
Senior AI/ML Engineer Benefits and Compensation:
- Choose the medical, dental, vision, and well\-being benefits that best fit your needs from day one.
- 8% employer contribution to retirement plan with an additional match up to 2% on day one.
- 22 days of PTO \+ 12 paid holidays.
- Hybrid working arrangement.
- The compensation range for this position is $162,000 – $202,500 with a bonus opportunity and customary benefits. Final compensation will be set based on the hired applicant’s qualifications (education, training, and/or experience related to this role), and as such, may fall outside the range shown.
Minimum Requirements/Qualifications:
- 7\+ years of professional software development experience, including 2\+ years of designing, developing, or deploying AI\-enabled solutions in enterprise environments.
- Experience delivering AI solutions that demonstrate measurable business outcomes such as productivity gains, process automation, knowledge discovery, service improvements, or cost reduction.
- Proficient in Python and commonly used AI/ML technologies.
- Experience designing, testing, deploying, and supporting production software solutions in enterprise environments.
- Working knowledge of modern AI and language model concepts and experience building applications with LLM APIs, prompt/context engineering, RAG, embeddings, vector search, and model evaluation.
- Understanding of agentic AI concepts including task planning, human\-in\-the\-loop review, orchestration, guardrails, and evaluation of agent behavior.
- Experience working with relational, document, or enterprise knowledge sources such as SQL Server, Oracle, Microsoft Fabric, SharePoint, or similar.
- Strong analytical, troubleshooting, documentation, and problem\-solving skills.
Preferred Qualifications
Experience with or exposure to:
- AI engineering tools such as GitHub Copilot, Claude Code, prompt management frameworks, evaluation tools, and AI operational tooling.
- AI platforms such as Azure AI Foundry, Azure AI Search, Copilot Studio, Amazon Bedrock, Amazon SageMaker, Amazon Q, OpenSearch, Pinecone, pgvector, or similar technologies supporting retrieval, semantic search, and AI application development.
- Applied machine learning and natural language processing techniques such as classification, entity extraction, semantic search, and model evaluation.
- Enterprise content preparation for AI, including document ingestion, metadata, embeddings, vector databases and semantic indexes, semantic search, retrieval validation, grounding strategies, and content lifecycle practices.
- Responsible AI, data governance, PHI/PII protection, access control, monitoring, secure development, and production support practices.
- Enterprise integration patterns, APIs, AWS Lambda\-based AI integrations, Azure Functions, containers, event\-driven workflows, automation tools, and human\-in\-the\-loop review processes.
*Wespath Benefits and Investments is an Equal Opportunity Employer and does not discriminate in hiring.*
Salary Context
This $162K-$202K range is above the median 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 Wespath Benefits and Investments, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($182K) sits 15% below the category median. Disclosed range: $162K to $202K.
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.
Wespath Benefits and Investments AI Hiring
Wespath Benefits and Investments has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Glenview, IL, US. Compensation range: $202K - $202K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.