Interested in this AI/ML Engineer role at Applied Research Solutions?
Apply Now →Skills & Technologies
About This Role
Applied Research Solutions (ARS) is seeking a skilled AI Systems Engineer to support, operate, and enhance the platforms that deliver AI capabilities across cloud and on prem environments. This role is focused on operational support, administration, and secure platform management—not AI research or model development.
The ideal AI Systems Engineer candidate will have experience managing M365 Copilot, Azure OpenAI, and on‑premise AI systems, with a strong understanding of how AI tools integrate with enterprise environments, how data flows through AI systems, and how to apply security and governance controls in a CMMC Level 2 setting. This role will also manage cost controls and model selection considerations to ensure efficient and predictable AI usage. The position offers opportunities to expand expertise across AI operations, hybrid cloud, Kubernetes, identity governance, and FinOps for AI while supporting mission‑critical programs.
Why Work with us?
Applied Research Solutions (ARS) is respected as a world-class provider of technically integrated solutions as we deliver premier talent and technology across our focused markets for unparalleled, continuous mission support. Awarded a Best Places to Work nominee since 2020, ARS recognizes that without our career- driven, loyal professionals, we would not be able to deliver state-of-the-art results for our mission partners. We firmly believe that prioritizing our employees is of the upmost importance. We provide a culture where our employees are challenged to meet their career goals and aspirations, while still obtaining a work/life balance. ARS employees are motivated through our industry competitive benefits package, our awards and recognition program, and personalized attention from ARS Senior Managers.
Responsibilities include:
- Administer and support AI platform workloads across M365 Copilot, Azure OpenAI, and on‑prem AI inference systems
- Operate AI‑related Azure services including identity (Entra ID), networking, private endpoints, resource policies, and secure service integrations
- Document, monitor, and secure end‑to‑end AI data flows, applying required controls to meet CMMC Level 2 compliance
- Assist with implementation of AI‑related security controls including identity governance, Conditional Access, RBAC, data classification, DLP, and auditing
- Implement usage monitoring, token tracking, quotas, and dashboards to control and optimize AI cost
- Provide operational support to developers using internal AI services, including templates, reference architectures, and troubleshooting
- Support hybrid cloud and on‑prem deployment of AI components, including containerized inference systems and Kubernetes workloads
- Maintain documentation, SOPs, diagrams, and compliance artifacts related to AI platform operations
- Continuously learn and adopt new AI platform tools, Azure features, and best practices related to secure and cost-efficient AI operations
- Other duties as assigned
Qualifications/ Technical Experience Requirements:
- Must be a US citizen
- Ability to obtain a Secret security clearance (active clearance is beneficial but not required)
- Bachelor’s degree in a technical and related field.
- 5+ years of cloud, systems, platform, or operational engineering experience
- Demonstrated hands‑on experience with M365 administration, Azure services, or Azure OpenAI platforms
- Familiarity with container platforms and Kubernetes
- Understanding of AI data flows, how AI systems integrate with enterprise platforms, and how to apply security controls to those data flows
- Knowledge of CMMC Level 2 or NIST SP 800‑171 security practices
- Knowledge of AI cost optimization strategies including quotas, token usage monitoring, model selection, and workload control
- Current Azure Administrator Associate (AZ‑104), or ability to obtain within six months of hire
- Security+ or other DoD 8140 IAM Level II certification required
- Strong organizational, communication, and multitasking skills
- Ability to thrive as part of a small, fast‑moving operational team
- Strong critical thinking, troubleshooting, and innovation skills
All positions at Applied Research Solutions are subject to background investigations. Employment is contingent upon successful completion of a background investigation including criminal history and identity check.
This contractor and subcontractor shall abide by the requirements of 41 CFR 60-741.5(a). This regulation prohibits discrimination against qualified individuals on the basis of disability, and requires affirmative action by covered prime contractors and subcontractors to employ and advance in employment qualified individuals with disabilities.
This contractor and subcontractor shall abide by the requirements of 41 CFR 60-300.5(a). This regulation prohibits discrimination against qualified protected veterans, and requires affirmative action by covered contractors and subcontractors to employ and advance in employment qualified protected veterans
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Applied Research Solutions, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Applied Research Solutions AI Hiring
Applied Research Solutions has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Beavercreek, OH, US, Bedford, MA, US. Compensation range: $215K - $215K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.