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Trustmark’s mission is to improve wellbeing – for everyone. It is a mission grounded in a belief in equality and born from our caring culture. It is a culture we can only realize by building trust. Trust established by ensuring associates feel respected, valued and heard. At Trustmark, you’ll work collaboratively to transform lives and help people, communities and businesses thrive. Flourish in a culture of diversity and inclusion where appreciation, mutual respect and trust are constants, not just for our customers but for ourselves. At Trustmark, we have a commitment to welcoming people, no matter their background, identity or experience, to a workplace where they feel safe being their whole, authentic selves. A workplace made up of diverse, empowered individuals that allows ideas to thrive and enables us to bring the best to our colleagues, clients and communities.
About the role
The Solution Engineer – Microsoft Copilot, M365 Security \& Governance is responsible for leading the technical strategy, administration, governance, and adoption roadmap for Microsoft Copilot capabilities across the enterprise. This role focuses on ensuring Copilot is deployed, managed, secured, and continuously optimized across Microsoft 365, Microsoft Purview, Microsoft Defender, Microsoft Security Copilot, GitHub Copilot, Copilot Studio, and related AI\-enabled productivity, security, and development platforms. This position serves as the technical owner and strategic advisor for enterprise Copilot enablement. The Solution Engineer will partner with Security, Compliance, Infrastructure, Collaboration, Application Development, Legal, Risk, and business stakeholders to define standards, manage controls, evaluate new capabilities, and ensure responsible AI usage across the Microsoft ecosystem. The role requires deep understanding of Microsoft 365 administration, identity and access management, data protection, information governance, security operations, and modern AI\-powered productivity platforms.
Responsible for leading the technical requirements, gathering, and design sessions with stakeholders as it relates to infrastructure service systems (cloud and/or on\-premises). Translates service and product vision to the MSP team, disseminating knowledge, technical requirements, and design documentation. Identifies and evaluates project constraints and risks. Researches and monitors new technologies, frameworks, and tools that would improve process and optimize technological capabilities to meet business needs. Drives MSP to scopes, designs, and presents Proof of Concepts that have potential, analyzing for technical feasibility and implementation risks. Ensures solutions are in alignment to existing enterprise architecture framework and business standards. Serves as a technical resource for design implementations across teams. Creates solution designs based on conceptual design and stated business requirements. Identifies and evaluates new technologies for implementation. Participates in service design meetings and analyzes user needs to determine technical requirements. Ensures solutions align to business and IT strategies. Participates in defining roadmaps for one or more infrastructure technical services. Maintains service life cycle management which consists of change, operational readiness, and new and deprecating technical features.
Key Accountabilities:
- Collaborate with leadership to define technical roadmaps and strategies.
- Drive innovation and explore new technologies to improve existing solutions.
- Lead the design of complex infrastructure service solutions.
- Approves specifications and sets standards.
- Collaborates with cross functional teams on the design implementation of infrastructure service solutions.
- Understands and solves business problems and provide alternative solutions.
- Collaborates with user to incorporate feedback and influence the application/product design.
Minimum Requirements:
- Bachelor’s Degree and/or 6 – 8 years of related experience.
The compensation range for this role is (based on the corporate location in Lake Forest, Illinois):
$102,662\.00 \- $190,658\.00 per year
The final salary offer will be determined based on factors such as location, qualifications, experience, skill set, and other relevant factors. This position may also be eligible for commission. We understand that compensation is an important factor when considering a new opportunity, and we strive to provide a competitive salary within the market.
Brand: Trustmark
Come join a team at Trustmark that will not only utilize your current skills but will enhance them as well. Trustmark benefits include health/dental/vision, life insurance, FSA and HSA, 401(k) plan, Employee Assistant Program, Back\-up Care for Children, Adults and Elders and many health and wellness initiatives. We also offer a Wellness program that enables employees to participate in health initiatives to reduce their insurance premiums.
Trustmark is committed to leveraging the talent of a diverse workforce to create great opportunities for our people and our business. We are an equal opportunity employer, including disability and protected veteran status.
Salary Context
This $102K-$190K range is below 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 HealthFitness, 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 $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 ($146K) sits 32% below the category median. Disclosed range: $102K to $190K.
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.
HealthFitness AI Hiring
HealthFitness has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lake Forest, IL, US. Compensation range: $190K - $190K.
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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