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About This Role
Andrew Morgan LLC (AM) is a fast\-growing Service\-Disabled Veteran\-Owned Small Business (SDVOSB) and HUBZone Small Business. We are seeking a motivated and technically skilled AI Engineer or Solution Architect to join our growing team. This is an opportunity to work at the intersection of artificial intelligence, cloud infrastructure and enterprise solutions in a dynamic, mission\-driven environment.
RESPONSIBILITIES:
This is a mid\-level position for a hands\-on developer or engineer with practical experience building and deploying AI\-powered solutions. You will work alongside senior architects and engineering leads to design, develop and deliver intelligent applications that solve real business problems. If you have a background in AI development, machine learning engineering or cloud\-based application development and are looking to grow your impact, we want to hear from you.
- Design, develop and deploy AI\-driven applications and solutions using platforms such as AWS Bedrock, Azure OpenAI, or similar
- Build and integrate machine learning models, large language model (LLM) workflows and intelligent automation into enterprise systems
- Collaborate with cloud architects to ensure AI solutions are scalable, secure and aligned with enterprise standards
- Develop and maintain APIs, pipelines and integrations that support AI and data\-driven applications
- Contribute to the design and documentation of solution architecture, data flows and technical roadmaps
- Support cloud infrastructure tasks related to AI workloads on AWS or Azure
- Research and evaluate emerging AI tools, frameworks and platforms to inform solution design
- Participate in Agile ceremonies and contribute to project planning, documentation and delivery
REQUIREMENTS:
- 3 to 5 years of experience in software development, AI engineering or a related technical discipline
- Bachelor's degree in Computer Science, Engineering, Data Science or a related field, or equivalent practical experience
- Hands\-on experience developing with AI or machine learning frameworks and tools (e.g., AWS Bedrock, Azure OpenAI, OpenAI API, LangChain, Hugging Face, or similar)
- Proficiency in one or more programming languages such as Python, JavaScript, Java or .NET
- Experience working with cloud platforms, preferably AWS or Azure
- Familiarity with REST APIs, microservices and cloud\-native application development
- Strong written and verbal communication skills with the ability to work across technical and non\-technical teams
PREFERRED QUALIFICATIONS:
- Experience with LLM orchestration, prompt engineering, retrieval\-augmented generation (RAG) or AI agent development
- Familiarity with data platforms such as Databricks, Redshift, Snowflake, S3 or Synapse Analytics
- Knowledge of Python, SQL, or data engineering concepts including ETL/ELT and data pipelines
- AWS or Azure certifications (e.g., Solutions Architect Associate, AI Practitioner, or equivalent)
- Experience in federal, regulated or compliance\-driven environments
- Exposure to DevOps practices, CI/CD pipelines and infrastructure as code
- Familiarity with enterprise data management, NoSQL or data warehousing concepts
SALARY:
Salary is commensurate with both location and experience.
THE AM EMPLOYEE WILL DISPLAY…
- Attention to Detail: Produce high\-quality deliverables and outputs that align with contract outcomes.
- Self\-Sufficiency: Execute tasks independently, seeking client assistance only after internal resources are exhausted, ensuring requests align with contract goals.
- Teamwork: Collaborate effectively with team members, including AMC resources, subcontractors, and government stakeholders.
- Communication/Engagement: Maintain professional conduct in meetings and written communication, participating actively with cameras on.
- Responsiveness: Stay engaged, communicate promptly, and ensure deadlines are consistently met.
ABOUT ANDREW MORGAN…
Andrew Morgan LLC, founded in 2017 and headquartered in Alexandria, VA, is a Service\-Disabled Veteran Owned Small Business (SDVOSB) and a certified HUBZone Small Business. We specialize in delivering high\-quality consulting services to both federal and commercial clients, offering expertise in Strategy and Management Consulting, Technology \& Architecture Services, and Industry \& Mission Analytics Solutions. Our distinguished clientele includes the Department of Veterans Affairs (VA), National Aeronautics and Space Administration (NASA), the Department of Defense (DoD), and the United Stated Army Corps of Engineers (USACE). We are committed to providing innovative solutions and exceptional service to support our clients' missions and objectives.
BENEFITS:
- 15 days of Paid Time Off \+ 11 Paid Federal Holidays
- 401K Program (up to 5% employer matching)
- Three Gold Healthcare Options (with 75%\-90% employer\-paid premiums)
- Flexible Spending Account / Dependent Care Assistance Program
- Employer\-Paid Short Term Disability Insurance
- Employer\-Paid Long Term Disability Insurance
- Employer\-Paid Life Insurance
- Professional Training and Development
- Corporate Team Building Events
- And MORE!
EQUAL OPPORTUNITY EMPLOYER:
Andrew Morgan does not discriminate in employment opportunities, terms and conditions of employment, or practices. All qualified applicants will receive consideration for employment without regard to race, age, gender, religious or political beliefs, national origin or heritage, disability, sexual orientation, protected veteran status, or any characteristic protected by law.
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 3,057 AI roles we're tracking, AI/ML Engineer positions make up 72% of the market. At andrew morgan, 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 $179,000 based on 11,905 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
andrew morgan AI Hiring
andrew morgan has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US.
Location Context
Across all AI roles, 17% (513 positions) offer remote work, while 2,528 require on-site attendance. Top AI hiring metros: New York (2,449 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,057 open positions tracked in our dataset. By seniority: 94 entry-level, 1,467 mid-level, 1,148 senior, and 348 leadership roles (Director, VP, C-Level). Remote roles make up 17% of the market (513 positions). The remaining 2,528 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,057 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,189), Data Scientist (233), AI Software Engineer (195). 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 (94) are outnumbered by mid-level (1,467) and senior (1,148) 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 348 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 17% of all AI roles (513 positions), with 2,528 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,566 postings), Aws (974 postings), Azure (725 postings), Rag (683 postings), Gcp (597 postings), Prompt Engineering (472 postings), Pytorch (461 postings), Claude (447 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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