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About This Role
AI Solutions Engineer
Join OM Group’s mission\-focused team at Rock Island Arsenal and help modernize the Army’s logistics software that keeps soldiers supplied and ready worldwide. You’ll work on a stable Monday to Friday schedule inside an agile environment, collaborating with cleared professionals who value craftsmanship, accountability, and continuous improvement. If you’re driven to solve real world challenges, eager to grow your skills while you coordinate project activities, support customer engagement, track action items and deliverables, manage schedules and risks, and help ensure successful execution of contract requirements., and are ready to make a direct impact on national readiness, we’d like to meet you.
The AI Solutions Engineer serves as the technical lead for designing, developing, deploying, and optimizing enterprise Artificial Intelligence solutions. This position develops production\-ready AI workflows, Large Language Model (LLM) integrations, intelligent agents, and decision\-support applications that transform enterprise operations while ensuring security, governance, and responsible AI practices.
The ideal candidate combines knowledge and expertise in AI/ML engineering, software development, data engineering, cloud architecture, and operational workflows to deliver secure, scalable AI solutions for government and commercial organizations.
Responsibilities
- Design and implement enterprise AI applications using Palantir AIP
- Build production\-grade AI workflows and autonomous agents
- Develop Retrieval\-Augmented Generation (RAG) architectures
- Create AI assistants for operational decision support
- Configure prompt engineering strategies for enterprise use cases
- Develop reusable AI components and services
- Optimize inference performance and model accuracy
- Fine\-tune LLM prompts
- Evaluate model performance
- Reduce hallucinations
Requirements
- Experience with connecting AI directly with operational workflows, enterprise data, and governance rather than deploying standalone models
- AIP Logic
- Pipeline Builder
- Data Lineage
- Operational Applications
- Code Repositories
- Foundry Data Engineering
- Foundry Object Types
- 5 – 7 years of software engineering or data engineering experience
- SEC\+: CompTIA Security\+ (IAT\-II)
- Active Secret Security Clearance
- Strong written and verbal communication skills
- Experience supporting federal government IT programs
- Experience supporting Department of Defense customers
- Experience working in Agile project environments
- Bachelor's degree in a business, technical, or related field
- Experience supporting software development or system modernization efforts
- Experience with the following competencies: Systems Thinking, Enterprise Architecture, Solution Design, Analytical Problem Solving, AI Strategy Development, Technical Leadership, Customer Engagement, Executive Communication, Agile Methodologies, Scrum, Design Thinking, Continuous Improvement, Palantir Artificial Intelligence Platform (AIP)
Accepted Certifications
- Microsoft Azure AI Engineer Associate (AI\-102\)
- Microsoft Azure Solutions Architect Expert (AZ\-305\)
- Microsoft Azure Developer Associate (AZ\-204\)
- Microsoft Azure Data Engineer Associate (DP\-203\)
- Microsoft Power Platform Developer (PL\-400\)
- Certified Kubernetes Application Developer (CKAD)
- Security\+ or CISSP (government environments)
- Palantir Foundry Certification (if available)
- Palantir AIP Bootcamp/Training
OM Group, Inc. is a growing company that values your skills, training, and ideas and strives to foster a welcoming environment. We provide competitive compensation and benefits including health insurance coverage, paid time off, as well as support for continuous education and training.
In compliance with pay transparency guidelines, the annual base salary target range for this position is $125,000 to $150,000\. Please note that the salary information is a general guideline only. OM Group considers factors such as (but not limited to) scope and responsibilities of the position, candidate’s work experience, education/training, key skills, internal peer equity, as well as market and business considerations when extending an offer.
OM Group, Inc. is an Equal Opportunity Employer (EOE) committed to compliance with all applicable federal, state, and local employment laws. We provide employment opportunities without regard to race, color, religion, sex, national origin, disability, veteran status, genetic information, or any other characteristic protected by law. OM Group, Inc. is dedicated to fostering a workplace that is free from unlawful discrimination and retaliation. Our hiring and employment practices are based on merit, ensuring that all individuals have equal opportunities based on their qualifications, experience, and skills.
If you need reasonable accommodation to apply, please contact [email protected]
No Third Parties or C2C Solicitation
This is a direct hire position ineligible for third party partnering. OM Group does not accept unsolicited resumes from third\-party recruiters without a signed third\-party agreement. Any unsolicited third\-party resumes forwarded by recruiters to OM Group or to any of our managers or employees will be considered public information, may be treated as a direct application from the person identified in the resume, and will not be eligible for placement fee payment to the agency.
Salary Context
This $125K-$150K range is below the median 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 Om Group Inc, 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 $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 ($137K) sits 37% below the category median. Disclosed range: $125K to $150K.
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.
Om Group Inc AI Hiring
Om Group Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Chicago, IL, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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