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About This Role
SMX is seeking an AI Engineer to support government client at Camp Smith. The AI Engineer designs, develops, tests, and improves AI\-enabled systems that support client operations, business processes, and organizational Knowledge Management. This role transforms business and operational processes into tools that can be integrated into AI harnesses, workflows, and agentic systems.
The ideal candidate has a balanced technical background and military operational experience. This combination allows the AI Engineer to understand real operational processes and convert them into practical AI\-integrated systems. The AI Engineer should be able to work with Maven Smart System as the primary platform while also understanding cloud environments, AI tools, data systems, and modern AI development patterns.
This role supports the creation of AI capabilities that may include AI\-integrated tools, retrieval workflows, organizational data models, internal LLM development, prompt systems, AI agents, and systems that use organizational data to improve decision support.
This position requires a current DoD Top Secret SCI security clearance which requires US citizenship for work on DoD contracts.
This position is full\-time onsite in Aiea, Hawaii.
Application Deadline: July 20, 2026
Essential Duties \& Responsibilities
- The AI Engineer builds the AI\-enabled technical capability that transforms client processes into integrated tools, workflows, and AI systems.
- Design, build, and support AI\-integrated systems that improve client workflows and decision\-making.
- Transform operational and business processes into tools that can be integrated into AI\-enabled environments.
- Support Maven Smart System as the primary AI and operational integration platform.
- Develop AI\-enabled workflows that connect organizational knowledge, data, tools, and decision processes.
- Support the design and development of in\-house LLM capabilities trained or tuned on approved organizational data, where authorized.
- Develop retrieval\-augmented generation workflows using trusted organizational data sources.
- Support AI agent workflows that retrieve information, summarize content, call tools, generate draft products, and support human decision\-making.
- Design prompt templates, prompt chains, structured outputs, and reusable AI patterns.
- Integrate AI systems with cloud environments, APIs, databases, knowledge repositories, dashboards, and workflow tools.
- Support embedding models, vector search, knowledge retrieval, data ingestion, and AI search capabilities.
- Develop methods to test and evaluate AI system performance, accuracy, relevance, and usefulness.
- Work with the AI Integrator and Knowledge Systems Integrator to ensure AI tools are operationally useful, adopted by users, and connected to real client workflows.
- Support AI governance, human\-in\-the\-loop review, data protection, and auditability.
Required Skills \& Experience
- Bachelor's degree in computer science, AI, Data Science, Software Engineering, Information Systems, Cybersecurity, Engineering, or related field.
- 5–8 years of relevant technical experience; additional experience may substitute for degree.
- Military operational experience is strongly preferred.
- Experience developing AI\-enabled systems, tools, workflows, automation, or data integrations.
- Experience with Maven Smart System as the primary AI/operational platform preferred.
- Strong understanding of cloud environments, AI tools, APIs, databases, and system integration.
- Experience with RAG, LLMs, AI agents, embeddings, vector search, prompt engineering, and AI evaluation.
- Ability to transform business and operational processes into AI\-integrated tools.
- Experience supporting in\-house LLMs or domain\-adapted AI systems using organizational data preferred.
- Must meet applicable DoD 8140 / DCWF requirements.
- IAT Level II or IAM Level I equivalent certification preferred; IAT Level III, IAM Level II, or IASAE\-equivalent preferred for elevated technical roles.
- Ability to test and evaluate AI outputs.
- Understanding of human\-in\-the\-loop review and responsible AI use.
- Military operational experience or strong understanding of military workflows.
Desired Skills \& Experience
- Experience with Palantir or Maven Smart System.
- Experience developing AI tools in cloud environments.
- Experience supporting operational, intelligence, or defense organizations.
- Experience with Python, APIs, data pipelines, or AI development frameworks.
- Experience creating RAG systems, AI agents, or prompt libraries.
- Experience with internal LLM development, fine\-tuning, or domain adaptation.
- Experience building AI systems around organizational data.
\#LI\-AP1 \#CJPOST
At SMX®, we are a team of technical and domain experts dedicated to enabling your mission. From priority national security initiatives for the DoD to highly assured and compliant solutions for healthcare, we understand that digital transformation is key to your future success.
We share your vision for the future and strive to accelerate your impact on the world. We bring both cutting edge technology and an expansive view of what's possible to every engagement. Our delivery model and unique approaches harness our deep technical and domain knowledge, providing forward\-looking insights and practical solutions to power secure mission acceleration.
SMX is an Equal Opportunity employer including disabilities and veterans.
Selected applicant may be subject to a background investigation and/or education verification.
SMX does not sponsor a new applicant for employment authorization or immigration related support for this position (i.e. H1B, F\-1 OPT, F\-1 STEM OPT, F\-1 CPT, J\-1, TN, E\-2, E\-3, L\-1 and O\-1, or any EADs or other forms of work authorization that require immigration support from an employer).
Salary Context
This $140K-$173K 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 SMX, 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 ($156K) sits 28% below the category median. Disclosed range: $140K to $173K.
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.
SMX AI Hiring
SMX has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, ‘Aiea, HI, US. Compensation range: $173K - $272K.
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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