Interested in this AI/ML Engineer role at VetsEZ?
Apply Now →Skills & Technologies
About This Role
VetsEZ is seeking a Senior AI Application Engineer to design, develop, and implement enterprise Artificial Intelligence (AI) solutions supporting the Department of Veterans Affairs (VA). The initial assignment will support the Joint Longitudinal Viewer (JLV) AI Summarization initiative, delivering a secure, governed AI\-assisted search and summarization capability within an approved test environment utilizing Retrieval\-Augmented Generation (RAG), Large Language Models (LLMs), and Amazon Bedrock, while supporting clinical evaluation and future production readiness.
Working closely with AI Solution Architects, Product Owners, cybersecurity teams, DevSecOps engineers, and clinical stakeholders, this individual will develop secure, scalable, and maintainable AI\-powered applications that integrate seamlessly with existing enterprise healthcare systems while ensuring compliance with Federal security, privacy, and AI governance requirements.
Responsibilities:
- Design, develop, and implement enterprise AI applications utilizing Large Language Models (LLMs) and Retrieval\-Augmented Generation (RAG).
- Develop AI\-assisted search, summarization, and question\-answering capabilities using Amazon Bedrock.
- Build reusable AI services supporting prompt orchestration, document retrieval, and response generation.
- Implement prompt engineering and source\-grounding strategies to improve AI accuracy, consistency, traceability, and clinical relevance, including source links that support human verification.
- Optimize AI performance while balancing response quality, latency, and operational cost.
- Design and develop secure, scalable cloud\-native applications utilizing modern software engineering practices.
- Develop RESTful APIs and backend services supporting AI capabilities and enterprise integrations.
- Implement approved document retrieval, vector search, and semantic search capabilities within the selected patient context and CHSD document set.
- Develop automated unit, integration, and functional tests and repeatable AI evaluations for groundedness, retrieval quality, and clinical relevance supporting AI\-enabled applications.
- Troubleshoot software defects, optimize application performance, and support activities within the approved test environment and for future production readiness.
- Integrate AI capabilities into existing enterprise healthcare applications and clinical workflows.
- Develop secure interfaces utilizing REST APIs and modern integration patterns.
- Support interoperability utilizing healthcare standards including FHIR, HL7, and CCD.
- Collaborate with Solution Architects and engineering teams to implement scalable and maintainable application designs.
- Participate in code reviews and promote software engineering best practices across the development team.
- Develop secure software in accordance with Federal cybersecurity and privacy requirements, including approved data\-retention and purge controls.
- Support CI/CD pipelines, automated deployments, and cloud\-native operational practices.
- Implement logging, monitoring, audit capabilities, and operational telemetry, including model and prompt version tracking, usage and cost monitoring, and controls to detect model, prompt, retrieval, and data drift.
- Support application security scanning, vulnerability remediation, and activities within the approved test environment and for future production readiness.
- Incorporate Responsible AI, Human\-in\-the\-Loop (HITL), and AI governance principles into application development.
- Collaborate with architects, product owners, clinicians, cybersecurity teams, and Government stakeholders throughout the software development lifecycle.
- Participate in Agile ceremonies including Sprint Planning, backlog refinement, Sprint Reviews, and Retrospectives.
- Contribute to technical documentation, implementation guides, and software design artifacts.
- Present technical solutions and implementation approaches to project leadership and stakeholders.
Requirements:
- Bachelor's degree in Computer Science, Software Engineering, Information Systems, Artificial Intelligence, Data Science, or a related technical field, or equivalent experience.
- 8\+ years developing enterprise software applications.
- 5\+ years developing cloud\-native applications utilizing AWS or comparable cloud platforms.
- Demonstrated experience developing Artificial Intelligence, Machine Learning, or Generative AI solutions.
- Experience implementing applications utilizing Amazon Bedrock or similar enterprise AI platforms.
- Experience developing enterprise REST APIs and cloud\-native application services.
- Amazon Bedrock and AWS cloud services
- Large Language Models (LLMs)
- Retrieval\-Augmented Generation (RAG)
- Prompt engineering and AI evaluation
- Python, Java, or C\#
- REST APIs and JSON
- Vector databases, embeddings, and semantic search
- Git, CI/CD, and DevSecOps
- Healthcare interoperability (FHIR, HL7, CCD)
Additional Qualifications:
- Strong understanding of modern software engineering principles and cloud\-native application development.
- Experience developing scalable, secure, and maintainable enterprise applications.
- Excellent analytical, troubleshooting, and problem\-solving skills.
- Strong written and verbal communication skills with the ability to collaborate across multidisciplinary engineering teams.
- Ability to obtain and maintain a Government Public Trust clearance.
- Experience supporting the Department of Veterans Affairs (VA), Department of Defense (DoD), or other Federal healthcare organizations.
- Experience developing AI\-enabled clinical workflow, information\-retrieval, or clinician\-support applications requiring human validation.
- Experience implementing vector search, embeddings, semantic search, and prompt orchestration.
- Knowledge of Responsible AI, NIST AI Risk Management Framework (AI RMF), NIST SP 800\-53, FISMA, and FedRAMP.
- Familiarity with clinical terminology standards including SNOMED CT, ICD\-10, RxNorm, and LOINC.
- AWS Developer, AWS AI, Machine Learning, or other AWS cloud certifications are highly desirable.
Benefits:
- Medical, Dental, and Vision Insurance
- 401(k) with Employer Match
- Paid Time Off plus Federal Holidays
- Corporate Laptop
- Professional Development and Training Opportunities
- Remote Opportunity
Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, or protected veteran status.
Sorry, we are unable to offer sponsorship at this time.
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 VetsEZ, 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.
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.
VetsEZ AI Hiring
VetsEZ has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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.