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About This Role
Job ID
327320
Job Title: Gen AI Developer
Job Category: Information Technology
Time Type: Full time
Minimum Clearance Required to Start: None
Employee Type: Regular
Percentage of Travel Required: Up to 10%
Type of Travel: Local
\* \* \* The Opportunity:
CACI is currently looking for a Gen AI Developer to join our BEAGLE (Border Enforcement Applications for Government Leading\-Edge Information Technology) Agile Solution Factory (ASF) Team supporting Customs and Border Control (CBP) client located in Northern Virginia! Join this passionate team of industry\-leading individuals supporting the best practices in Agile Software Development for the Department of Homeland Security (DHS).
As a member of the BEAGLE ASF Team, you will support the men and women charged with safeguarding the American people and enhancing the Nation’s safety, security, and prosperity. CBP agents and officers are on the front lines, every day, protecting our national security by combining customs, immigration, border security, and agricultural protection into one coordinated and supportive activity.
ASF programs thrive in a culture of innovation and are constantly seeking individuals who can bring creative ideas to solve complex problems, both technical and procedural at the team and portfolio levels.
You will excel at developing innovative software solutions as part of a technically diverse and geographically disbursed team. A strong passion for AI technologies and experience with agile delivery and deploying software in short sprints.
Responsibilities:
- Design, develop, and implement custom software solutions for a variety of AI/ML\-related pilot projects and use cases.
- Act as a member of one or more prototyping teams supporting teammates and collaborating to deliver working software applications on short timelines.
- Analyze complex project\-related problems and creating innovative solutions involving technology, methodology, tools, and solution components.
- Actively participate in agile delivery phases and ceremonies including release and sprint planning, artifact creation, sprint testing, demonstrations, and retrospectives
- Staying current with the latest advancements in AI technologies, tools, and best practices.
- Design systems that can scale horizontally and handle high availability
- work with Data Scientist teams to integrate applications with AI/ML, Big Data, or BI solutions
- Ability to work independently on a complex task with little direction and management oversight.
Qualifications:
*Required:*
- Must be a U.S. Citizen with the ability to pass CBP background investigation, criteria include, but not limited to:
- 3\-year check for felony convictions
- year check for illegal drug use
- year check for misconduct such as theft or fraud
- BA/BS Degree. Will consider experience in lieu of degree.
- 7 or more years of full stack software development experience or related role which includes experience with front\-end frameworks such as React / Angular, and possess a comprehensive knowledge of both SQL and NoSQL databases
- Familiarity with fundamental Generative AI concepts, including Large Language Models (LLMs), transformers, and prompt engineering techniques.
+ Exposure to or basic practical experience with relevant GenAI frameworks and libraries
+ Experience working with APIs for commercial or open\-source models
- Ability to analyze technically complex problems and develop and implement new and innovative solutions in a fast\-moving and fluid software prototyping development environment.
- Expertise in multiple software developer languages including, but not limited to: Python, Java, JavaScript, and TypeScript
- Demonstrated experience with cloud services and platforms (AWS and Google) which includes an understanding of how to deploy, manage, and scale applications
- Familiarity with cloud architecture
- Knowledge of virtual networks, load balancers, and content delivery networks (CDNs)
- Experience working with DevSecOps.
- Ability to develop and work with APIs including familiarity with RESTful services, knowledge of authentication methods, familiarity with data formats, and skilled in handling API errors.
- Ability to understand API development and management.
- Work hybrid schedule with an on\-site requirement in Ashburn, VA
*Desired:*
- Technical certifications such as AWS Certified Developer and AWS AI Certified Practitioner
- Experience with DevOps (CI/CD) solutions such as GitLab, etc.
- Experience with AI/ML solutions or complex solutions for predictive analytics, such as Google Vertex AI and AWS Bedrock
- Experience working in a geographically dispersed, remote team.
- Understanding of Data Science principles and best practices.
*
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.
Since this position can be worked in more than one location, the range shown is the national average for the position.
The proposed salary range for this position is:
$90,300\-$189,600*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 $90K-$189K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($139K) sits 23% below the category median. Disclosed range: $90K to $189K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
CACI International AI Hiring
CACI International has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Prompt Engineer, Research Engineer. Positions span Norfolk, VA, US, Remote, US, Reston, VA, US. Compensation range: $115K - $290K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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