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About This Role
Company Summary
Targeted Approach is an ISO 9001:2015-certified Service-Disabled Veteran-Owned Small Business (SDVOSB) providing technical, analytical, logistics, management, and mission support services to customers across the Department of Defense and Federal Government.
Targeted Approach is an Equal Opportunity Employer. Employment decisions are based on qualifications, merit, and mission requirements without regard to legally protected characteristics.
Position Summary
Targeted Approach (TA) is seeking an Intermediate Artificial Intelligence (AI) Engineer to support a Department of Defense program at Marine Corps Air Station (MCAS) Cherry Point, North Carolina. The Intermediate AI Engineer will provide hands-on technical expertise in artificial intelligence engineering, machine learning model development, cloud engineering, systems deployment, automation, security compliance, performance monitoring, quality assurance, and technical support.
The successful candidate will bridge data science, artificial intelligence, cloud technologies, and software engineering to design, develop, test, deploy, and sustain production-ready AI/ML solutions. This position will support the transition of AI capabilities from development and experimentation into reliable, secure, and scalable operational environments.
Duties
- Design, build, test, evaluate, and deploy machine learning models and artificial intelligence applications that automate tasks, improve business and operational processes, and address complex technical problems.
- Apply data science and software engineering principles to develop production-ready AI/ML systems capable of operating reliably within DoD environments.
- Develop and maintain software, scripts, data pipelines, and AI/ML solutions using Python, SQL, and other applicable programming and query languages.
- Develop AI/ML solutions using frameworks and libraries such as TensorFlow, PyTorch, or comparable technologies.
- Design, build, configure, and deploy AI applications and supporting infrastructure within cloud environments such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), or similar platforms.
- Design, build, configure, and maintain virtualized and cloud-based systems supporting organizational data, applications, AI capabilities, and infrastructure.
- Support the migration and modernization of on-premises applications, data, and systems to cloud-based environments.
- Develop and implement automation for cloud infrastructure, data pipelines, AI/ML workflows, deployment processes, and recurring technical activities to improve efficiency, scalability, repeatability, and reliability.
- Support applicable DoD cybersecurity, information assurance, and security compliance requirements for AI, cloud, and virtualized environments.
- Monitor AI models, applications, systems, and cloud environments to evaluate performance, scalability, reliability, availability, and operational effectiveness.
- Troubleshoot technical issues associated with AI/ML applications, cloud environments, data pipelines, interfaces, and deployed systems.
- Perform testing, validation, documentation, configuration management, and quality assurance activities throughout the AI/ML development and deployment lifecycle.
- Collaborate with data analysts, software developers, cloud engineers, cybersecurity personnel, Government stakeholders, and other technical SMEs to translate operational requirements into effective technical solutions.
- Develop and maintain technical documentation supporting system architecture, AI/ML models, cloud configurations, interfaces, deployment procedures, automation, testing, and sustainment.
Experience
- Demonstrated experience designing, building, testing, and deploying machine learning models and artificial intelligence applications.
- Experience integrating data science and software engineering concepts to create production-ready AI systems.
- Experience programming and querying using Python and SQL.
- Experience using AI/ML frameworks such as TensorFlow, PyTorch, or similar tools.
- Experience designing, building, and deploying AI systems within AWS, Azure, GCP, or comparable cloud platforms.
- Experience designing, building, and maintaining virtualized and cloud-based environments supporting enterprise data, applications, and infrastructure.
- Experience supporting the migration of on-premises systems and applications to cloud environments.
- Experience automating cloud, infrastructure, data, and AI/ML processes.
- Experience supporting cybersecurity and security compliance requirements applicable to AI, cloud, and virtualized environments.
- Experience monitoring and optimizing system, model, application, and cloud performance to support scalability, reliability, and operational effectiveness.
Education Requirements
No degree requires 12 years of general experience
Associate's degree requires 8 years of general experience
Bachelor's degree 7 years of general experience
Master's degree 6 years of general experience
Ph.D. 4 years of general experience
Relevant degrees may include Artificial Intelligence, Machine Learning, Computer Science, Data Science, Software Engineering, Computer Engineering, Information Technology, Information Systems, or another related technical discipline.
Preferred Qualifications
- MLOps and AI/ML lifecycle management.
- DevSecOps and CI/CD pipelines.
- Infrastructure as Code (IaC) and automated cloud provisioning.
- Docker, Kubernetes, or other containerization/orchestration technologies.
- Cloud-native data storage, processing, and analytics services.
- REST APIs and integration of AI/ML capabilities with enterprise applications.
- Model versioning, validation, monitoring, retraining, and performance optimization.
- Git or comparable source-code/configuration management tools.
- DoD cloud environments and cloud security requirements.
- Risk Management Framework (RMF), Security Technical Implementation Guides (STIGs), or other DoD cybersecurity requirements.
- Working within DoD, Department of the Navy, or U.S. Marine Corps technical environments.
- Supporting AI/ML capabilities through development, testing, deployment, operation, and sustainment
Join us if you’re passionate about harnessing the power of artificial intelligence to transform data into impactful insights! We value innovative thinkers eager to develop next-generation AI models that shape the future of technology-driven solutions.
Pay: $110,000.00 - $130,000.00 per year
Benefits:
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Referral program
- Vision insurance
Education:
- Bachelor's (Preferred)
Experience:
- AI/ ML solutions: 7 years (Required)
Security clearance:
- Secret (Required)
Ability to Commute:
- Cherry Point, NC 28533 (Required)
Work Location: In person
Salary Context
This $110K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Targeted Approach, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($120K) sits 43% below the category median. Disclosed range: $110K to $130K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Targeted Approach AI Hiring
Targeted Approach has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cherry Point, NC, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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