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About This Role
AI Engineer
Temporary Assignment (through 2/13/2027\)
Remote, USA; potential for minimal ad hoc travel
EMKS is seeking an AI Engineer to support the Department of Veterans Affairs (VA) Office of Information Technology (OIT). This position will lead the design, development, and implementation of Artificial Intelligence (AI), Generative AI, Machine Learning (ML), and intelligent automation solutions that enhance enterprise software engineering, testing, DevSecOps, and IT operations.
The successful candidate will collaborate with cross\-functional teams to identify high\-value AI opportunities, develop proof\-of\-concepts, and deliver secure, scalable, production\-ready AI solutions aligned with VA AI governance and Trustworthy AI principles.
Duties/Responsibilities:
- Design and develop AI\-powered applications using Large Language Models (LLMs), Generative AI, and Machine Learning.
- Build AI Copilots, AI Agents, and Retrieval\-Augmented Generation (RAG) solutions to improve software engineering, testing, documentation, and operational workflows.
- Develop intelligent automation solutions for test case generation, defect analysis, knowledge management, predictive analytics, and process optimization.
- Integrate AI capabilities into enterprise applications, APIs, Azure cloud services, and DevSecOps pipelines.
- Evaluate emerging AI technologies and rapidly develop Proofs of Concept (PoCs).
- Collaborate with architects, engineers, testers, and business stakeholders to identify and implement innovative AI solutions.
- Ensure all AI solutions comply with VA cybersecurity, privacy, accessibility, and Trustworthy AI requirements.
Required Qualifications:
- 5\+ years in overall software engineering experience.
- 2\+ years of hands\-on experience developing AI, Machine Learning, or Generative AI solutions.
- Strong programming experience with Python.
- Experience with Azure OpenAI, OpenAI APIs, or similar LLM platforms.
- Experience with LangChain, LlamaIndex, Semantic Kernel, or comparable AI frameworks.
- Experience building Retrieval\-Augmented Generation (RAG) applications and AI Agents.
- Experience developing REST APIs using FastAPI or similar frameworks.
- Familiarity with Git, Azure DevOps, CI/CD, Docker, and cloud\-native development.
- Strong analytical, communication, and problem\-solving skills.
Preferred Qualifications:
- Experience supporting the U.S. Department of Veterans Affairs (VA) or other Federal agencies.
- Experience with healthcare IT, EHRM, VistA, Oracle Health Millennium, FHIR, or HL7\.
- Experience with Azure AI Studio, Azure Machine Learning, vector databases, and prompt engineering.
- Knowledge of NIST AI Risk Management Framework, Responsible AI, and Federal AI governance.
Education/Technical Skills:
- Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or related field.
- Python • Azure OpenAI • OpenAI APIs • LangChain • LangGraph • LlamaIndex • Semantic Kernel • RAG • AI Agents • Machine Learning • FastAPI • Azure AI Studio • Azure DevOps • Docker • Kubernetes • SQL • Git • REST APIs • Vector Databases
Clearance:
- S. Citizenship is requiredas is successfully passing a thorough Government background screening (Public Trust) requiring the completion of detailed forms and fingerprinting.
- Ability to obtain and maintain necessary security clearances as required for access to classified information.
About EM Key Solutions:
Founded in 2015, EM Key Solutions, Inc. (EMKS) is a Service\-Disabled Veteran\-Owned Small Business (SDVOSB) offering a broad range of services to support Federal Government enterprises in meeting their mission requirements and business demands. Relationships are key to EMKS! We adopt a customer\-centric approach and proven management processes for every project we undertake. Through sound leadership and management principles, EMKS focuses on offering its clients the solutions they need to be successful at the most competitive rates throughout the project management lifecycle.
If you are curious to learn more about EM Key Solutions, please visit our website at EMKS.com
EMKS is an Equal Opportunity Employer committed to hiring and retaining qualified and talented individuals. All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status.
Reasonable Accommodation Requests
EM Key Solutions is committed to working with and providing reasonable accommodation to individuals with physical and mental disabilities. If you need special assistance or accommodation while seeking employment, please e\-mail [email protected] or call Human Resources at (727\)\-292\-1521\. We will make a determination on your request for reasonable accommodation on a case\-by\-case basis.
E\-Verify
As a Federal Contractor, we are required to use E\-Verify to validate employees' ability to work legally in the United States.
EEO is the Law
The law requires EM Key Solutions to post a notice describing the Federal laws prohibiting job discrimination.
Pay Transparency Non\-Discrimination
EM Key Solutions will not discharge or, in any other manner, discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay.
For information regarding your legal rights and protections, please click on the following link: State and Federal Labor Notices
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 EM Key Solutions 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.
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.
EM Key Solutions Inc AI Hiring
EM Key Solutions Inc has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Myrtle Point, OR, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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