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About This Role
SMX is seeking an AI Engineer and Integrator to support government client at Camp Smith. The AI Integrator serves as the client\-facing AI integration lead and secondary AI Engineer. This role sits close to the client and is responsible for ensuring that AI tools, processes, applications, and systems are accurate, adopted, operationally useful, and aligned with client needs.
The AI Integrator leads the Knowledge Systems Integrator team's development efforts for new tools, processes, and applications. This role works directly with the client to identify where AI can improve operational planning, dynamic data analysis, business processes, and decision\-making workflows.
This position should also be classified as an AI Engineer because it requires technical AI understanding, tool development leadership, AI workflow design, and the ability to work with the primary AI Engineer on AI\-enabled systems. The difference is that the AI Integrator is more client\-facing and adoption\-focused, while the AI Engineer is more technically focused on system development.
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 Integrator leads the client\-facing integration of AI tools, systems, processes, and applications into operational and business workflows.
- Serve as the client\-facing lead for AI integration, adoption, and workflow alignment.
- Lead the Knowledge Systems Integrator team's development of new tools, processes, workflows, and applications.
- Work with the AI Engineer to design and refine AI\-integrated systems.
- Ensure AI tools are accurate, useful, trusted, and aligned to client requirements.
- Translate client operational needs into AI use cases, tool requirements, workflow designs, and adoption plans.
- Integrate AI capabilities into military joint planning processes.
- Support AI integration into dynamic environments that include large data streams, operational information, and changing mission variables.
- Identify business processes that can be improved through AI, automation, and decision\-support tools.
- Develop user\-facing AI workflows, prompt guides, playbooks, and SOPs.
- Define human\-in\-the\-loop review points, approval chains, validation steps, and escalation processes.
- Lead user adoption, training, feedback collection, and continuous improvement.
- Coordinate with the Knowledge Systems Integrator to ensure knowledge repositories and collaboration environments support AI workflows.
- Coordinate with the AI Engineer to ensure AI capabilities are technically sound and operationally relevant.
- Measure whether AI\-enabled tools improve workflow performance, decision speed, knowledge reuse, and operational outcomes.
Knowledge Areas
The AI Integrator should be familiar with or able to support:
- Military joint planning
- Operational workflows
- AI\-enabled decision support
- Maven Smart System
- Knowledge Management systems
- Dynamic data environments
- Business process automation
- AI adoption and training
- Tool development leadership
- Human\-machine teaming
- AI governance and review processes
Required Skills \& Experience
- Bachelor's degree in computer science, AI, Data Science, Information Systems, Systems Engineering, Military Studies, Intelligence Studies, Operations Research, Cybersecurity, or related field.
- 6–10 years of relevant experience; additional experience may substitute for degree.
- Military operational planning experience strongly preferred.
- Experience leading AI integration, tool development, process improvement, or mission technology efforts.
- Strong understanding of the Military Joint Planning Process and operational decision\-making.
- Experience integrating AI into joint planning, dynamic data environments, big data streams, and business processes.
- Experience with Maven Smart System, AI agents, RAG, prompt workflows, human\-in\-the\-loop review, and AI\-enabled decision support.
- Ability to lead the KSI team's development of new tools, processes, applications, and AI workflows.
- Ability to serve as the client\-facing lead for AI adoption, validation, training, and workflow integration.
- Must meet applicable DoD 8140 / DCWF requirements.
- IAM Level II or IAT Level II equivalent certification preferred; IAM Level III, IAT Level III, or IASAE\-equivalent preferred for senior lead roles.
- AWS certifications preferred: AWS Cloud Practitioner, Solutions Architect – Associate, Machine Learning Engineer – Associate, Machine Learning – Specialty, Security – Specialty, Developer – Associate, or Solutions Architect – Professional.
- Technical fluency in Python, SQL, JavaScript/TypeScript, APIs, cloud AI tools, databases, dashboards, automation, and low\-code/no\-code platforms.
- Leadership experience managing technical teams, development priorities, client working groups, AI adoption, and stakeholder communication required.
Desired Skills \& Experience
- Experience with Maven Smart System or Palantir\-related platforms.
- Experience as an AI Engineer, AI Integrator, or technical product lead.
- Experience supporting military staffs, planning teams, operations centers, or intelligence workflows.
- Experience integrating AI into joint planning or operational decision cycles.
- Experience leading low\-code/no\-code tool development.
- Experience with cloud AI services, data platforms, or automation tools.
- Experience developing AI adoption plans, prompt playbooks, and AI SOPs.
\#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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