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About This Role
Job Title: AI Engineer Job Category: Information Technology Time Type: Full time Minimum Clearance Required to Start: Employee Type: Regular Percentage of Travel Required: Up to 10% Type of Travel: Local
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The Opportunity:
We are seeking an AI Engineer to design, develop, test, and support AI-enabled applications and agent-based solutions to support the United States Secret Service (USSS) contract supporting the Department of Homeland Security. The engineer will work across the software development lifecycle, translating mission and user needs into secure, reliable capabilities that integrate AI models with enterprise data, applications, and workflows. The work may use frameworks such as LangChain and Microsoft Agent Framework, along with cloud-based AI services and established software engineering practices.
The AI Engineer will collaborate with product, functional, data, cybersecurity, and infrastructure teams to develop practical solutions that meet user needs and enterprise requirements. The role includes building and evaluating capabilities such as retrieval-augmented generation (RAG), tool and API integrations, and agent workflows; improving system quality through testing and user feedback; and supporting deployment, monitoring, and continuous improvement.
Responsibilities:
The Opportunity:
We are seeking an AI Engineer to design, develop, test, and support AI-enabled applications and agent-based solutions to support the United States Secret Service (USSS) contract supporting the Department of Homeland Security. The engineer will work across the software development lifecycle, translating mission and user needs into secure, reliable capabilities that integrate AI models with enterprise data, applications, and workflows. The work may use frameworks such as LangChain and Microsoft Agent Framework, along with cloud-based AI services and established software engineering practices.
The AI Engineer will collaborate with product, functional, data, cybersecurity, and infrastructure teams to develop practical solutions that meet user needs and enterprise requirements. The role includes building and evaluating capabilities such as retrieval-augmented generation (RAG), tool and API integrations, and agent workflows; improving system quality through testing and user feedback; and supporting deployment, monitoring, and continuous improvement.
Responsibilities:
- Design, develop, test, document, and maintain AI-enabled applications and services.
- Implement AI and agent workflows using LangChain, Microsoft Agent Framework, or other suitable frameworks and services.
- Develop RAG capabilities, including data ingestion, retrieval, grounding, and integration with authorized enterprise information sources.
- Integrate AI models with APIs, enterprise applications, and business workflows, including appropriate tool use and human review.
- Work with product owners, users, and functional specialists to understand needs and translate them into technical designs, user stories, and acceptance criteria.
- Develop and maintain application components, APIs, and services using Python and established software engineering practices.
- Create and execute automated tests and evaluations to assess accuracy, relevance, grounding, safety, and performance.
- Troubleshoot application and model behavior; use test results, telemetry, and user feedback to improve solution quality.
- Support deployment and operations, including CI/CD, monitoring, documentation, incident resolution, and ongoing maintenance.
- Apply security and privacy requirements, including identity and access controls, authorization-aware retrieval, and protection of sensitive data.
- Collaborate with data, cloud, cybersecurity, DevSecOps, and software engineering teams to integrate solutions into the enterprise environment.
- Contribute reusable components, technical documentation, and engineering practices that improve consistency and maintainability.
- Familiarity with AI safety frameworks such as the NIST AI Safety Framework to support implementation of alignment controls in artificial intelligence LLMs and agents
- Contribute to the development of the AI Safety/Alignment policies, procedures, etc., for ensuring ethical, controllable, and aligned artificial intelligence systems and agents
Qualifications:
Required:
- Bachelor's degree + 10 years of experience in artificial intelligence, machine learning, software engineering, or related field; equivalencies considered (Master's + 7 years; 16 years with no degree; AA + 12 years)
- At least five years of experience in software engineering, application development, AI/ML engineering, or a related technical field.
- Demonstrated experience developing software applications or services using Python and APIs.
- Hands-on experience building generative AI or machine learning applications that integrate models with data, applications, or workflows.
- Experience using LangChain, Microsoft Agent Framework, or a comparable framework to build AI or agent-based capabilities.
- Understanding of RAG, prompt and context design, model or application evaluation, and integration with enterprise data.
- Experience with automated testing, version control, and CI/CD practices.
- Familiarity with cloud-based application development and deployment; experience with Microsoft Azure is preferred.
- Understanding of secure software development and the need to protect sensitive information and restrict access to enterprise data.
- Ability to work collaboratively with technical teams, product staff, and functional stakeholders, and to communicate technical work clearly.
Desired:
- Experience developing production applications using both LangChain and Microsoft Agent Framework.
- Experience with LangGraph or other approaches for building stateful, multi-step agent workflows.
- Experience with Azure OpenAI, Azure AI services, or Azure AI Foundry.
- Experience implementing authorization-aware RAG, data filtering, and access controls for retrieval and tool use.
- Experience with AI evaluation methods, test sets, tracing, observability, and monitoring for quality, latency, and cost.
- Experience integrating AI applications with enterprise systems, microservices, and web interfaces.
- Familiarity with responsible AI practices, including human review, explainability, and safe handling of uncertain or unsupported outputs.
- Relevant cloud, AI, or software engineering certifications.
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What You Can Expect:
A culture of integrity.
At CACI, we place character and innovation at the center of everything we do. As a valued team member, you’ll be part of a high-performing group dedicated to our customer’s missions and driven by a higher purpose – to ensure the safety of our nation.
An environment of trust.
CACI values the unique contributions that every employee brings to our company and our customers - every day. You’ll have the autonomy to take the time you need through a unique flexible time off benefit and have access to robust learning resources to make your ambitions a reality.
A focus on continuous growth.
Together, we will advance our nation's most critical missions, build on our lengthy track record of business success, and find opportunities to break new ground — in your career and in our legacy.
Pay Range :
There are a host of factors that can influence final salary including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at CACI that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits and learning and development opportunities. Our broad and competitive mix of benefits options is designed to support and protect employees and their families. At CACI, you will receive comprehensive benefits such as; healthcare, wellness, financial, retirement, family support, continuing education, and time off benefits.
The proposed salary range for this position is:
$120,800 - $265,800 CACI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, age, national origin, disability, status as a protected veteran, or any other protected characteristic.
Salary Context
This $120K-$265K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CACI International, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($193K) sits 9% below the category median. Disclosed range: $120K to $265K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
CACI International AI Hiring
CACI International has 6 open AI roles right now. They're hiring across AI/ML Engineer, Prompt Engineer. Positions span Aberdeen Proving Ground, MD, US, Laredo, TX, US, Washington, DC, US. Compensation range: $101K - $265K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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