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About This Role
Nakupuna Consulting seeks an AI Solutions Engineer for an Amazon Web Services (AWS) environment with strong technical expertise in constructing, developing and extending Retrieval\-Augmented Generation (RAG) models. This individual will help the Defense POW/MIA Accounting Agency (DPAA) imagine and scope AI/ML use cases that will create the greatest value for the agency, define paths to navigate technical challenges, develop proof\-of\-concepts, and make plans for launching solutions at scale. The AI Engineer will design models using data from DPAA’s Mission Application Suite – like military personnel case files and excavation site reports – to further identify missing personnel from previous conflicts. The position collaborates closely with customers and mission partners to develop innovative solutions that enhance DPAA’s global efforts to locate, recover, and identify missing personnel through cutting\-edge technologies and cross\-functional teamwork. The AI Engineer will also collaborate with contractors and government IT professionals to accomplish system architecture and sustainment objectives.
The following reflects management’s definition of essential functions for this job but does not restrict the tasks that may be assigned.
Technical Responsibilities:* Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions addressing real\-world challenges.
- Engage directly with customers to understand their business problems and assist in the implementation of generative AI solutions.
- Brief customers and guide them on adoption strategies and production deployment pathways.
- Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations tailored to technical, business, and executive audiences.
- Provide customer and market feedback to product and engineering teams to influence product direction and roadmap.
- Implement data pipelines, vectorization methods, and API integrations using Python.
- Utilize AWS AI/ML services such as Amazon SageMaker, Bedrock, and Comprehend for model development, fine\-tuning, and orchestration.
- Manage structured and unstructured data using AWS storage and retrieval solutions like Amazon S3, OpenSearch, and Aurora.
- Apply vector database technologies such as FAISS, Pinecone, or OpenSearch vector search for document retrieval and similarity search.
- Integrate LLM APIs and apply prompt engineering techniques to connect retrieval systems with generative models.
- Demonstrate basic DevOps and security awareness on AWS, including IAM roles, networking (VPCs), and data encryption practices.
- Use containerization and orchestration tools such as Docker and AWS Lambda/ECS for scalable RAG (Retrieval\-Augmented Generation) pipeline deployment.
- Utilize AWS Lambda to run responses triggered by alerts from integrated services.
- Support security and operations using AWS\-native technologies, ensuring compliance with DoD security requirements.
- Evaluate and propose new AWS technologies and modernization paths to enhance operational efficiency and security with AI solutions.
- Develop and maintain Standard Operating Procedures (SOPs), technical documentation, and reports on system operations.
- Adhere to Agile program best practices such as participation and management of team scrums and sprint plannings.
- Document and track all work using the program approved ticketing system.
Skills/Qualifications:* Strong technical expertise in AWS GovCloud and Microsoft Windows Server environments.
- Excellent technical, organizational, decision\-making, and analytical skills.
- Strong communication skills with the ability to engage both technical staff and executive stakeholders.
- Ability to enforce program best practices (ticketing, documentation, reporting).
- Familiarity with DoD IT environments, compliance requirements, and cybersecurity standards.
- Ability to adapt to a dynamic, fast\-paced environment and support teams across geographically dispersed locations.
Education/Experience:* Bachelor’s degree in a STEM field from an accredited institution, or equivalent years of related work or military experience.
- Minimum of 3 years of relevant professional experience, including systems administration and cloud operations.
- Experience supporting DoD IT environments preferred.
- Experience using Agile methodologies and ticketing systems such as JIRA is desirable.
Preferred Certification(s):* Active IAT II Certification which may include CompTIA Advanced Security Practitioner (CASP\+), CompTIA Cybersecurity Analyst (CySA\+), Certified Information Systems Security Professional (CISSP), or CompTIA Security\+.
- AWS certification (e.g., AWS Certified AI Practitioner, AWS Certified Cloud Practitioner, AWS SysOps Administrator Associate, AWS Solutions Architect Associate)
Clearance Requirements: Must currently have at least a Secret level security clearance in Hawaii and TS/SCI in Arlington, Virginia. Must be a U.S. citizen. Work Location:* Position may be based in either Arlington, VA or Honolulu, HI.
- Preferred: on\-site three (3\) days per week. Will consider fewer days on\-site for the correct candidate.
- Occasional travel (1–2 weeks per year) within the Continental U.S. or to Hawaii may be required.
- This position may require working outside of normal business hours, including evenings and weekends, to meet operational or project demands.
Physical Requirements: The ideal candidate must at a minimum be able to meet the following physical requirements of the job *with or without reasonable accommodation:** Ability to perform repetitive motions with the hands, wrists, and fingers.
- Ability to engage in and follow audible communications in emergency situations.
- Ability to sit for prolonged periods at a desk and working on a computer.
The Nakupuna Companies use a market\-based compensation strategy to ensure that our employees are compensated within applicable market ranges commensurate with multiple factors, including but not limited to the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract\-specific affordability, organizational requirements, and position location. The projected compensation range for this position is $115,000\.00 to $145,000\.00 (annualized USD). The salary range displayed represents the typical salary range for this position and is just one component of Nakupuna Companies total compensation package for employees.
Salary Context
This $121K-$188K 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 Nakupuna Companies, 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 ($154K) sits 29% below the category median. Disclosed range: $121K to $188K.
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.
Nakupuna Companies AI Hiring
Nakupuna Companies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Arlington, VA, US. Compensation range: $188K - $188K.
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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