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About This Role
Job Title: AI & Data Use-Case Project Manager Job Category: Project and Program Management 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:
CACI is seeking an experienced AI & Data Use-Case Project Manager to support the Department of Homeland Security (DHS) Office of the Inspector General (OIG). This role offers a unique opportunity to decide where artificial intelligence earns its place in federal oversight work, by connecting the people who do audits, investigations, and inspections with the AI pilots that could make that work faster and better.
As the AI & Data Use-Case Project Manager, you will work with the Chief Data Officer's analysts, OIG program offices, and Government Product Owners to find the workflow problems AI can solve, shape the strongest ideas into pilot candidates, and carry each pilot through to a decision the Government can act on. Candidate pilots include AI analysis of investigative hotline data, AI-assisted investigative queries, an eDiscovery supplement for large collections of files, and translation and transcription. You will write the case for each candidate, secure approval for the test data it needs, prepare the selection and go/no-go packages, and report what each pilot was worth. AI engineers build the pilots and a Scrum Master runs the delivery cadence; your job is to make sure the team builds the right things and that leaders can see the return. The position is primarily remote, with on-site work at DHS OIG headquarters in Washington, DC as requested, during core hours of 7:00 AM to 7:00 PM Eastern. Join us to help an oversight agency adopt AI deliberately, with evidence behind every step.
Responsibilities:
- Lead use-case discovery with the Chief Data Officer's analysts and OIG program offices, including audit, investigations, inspections, and counsel, to identify workflow pain points where AI or analytics could help, and maintain a prioritized pipeline of candidate uses
- Write the case for each pilot candidate, covering the user problem, expected benefit, data needed and its sensitivity, feasibility input from the AI engineers, and success criteria a Government Product Owner can verify
- Prepare pilot selection packages for Government decision, and coordinate reviews with OCIO, the Chief Data Officer, OIG cybersecurity and privacy staff, and program office leadership
- Request and track approval of the test data each pilot needs through Government data owners and the COR, recording where approved data may be held, who may use it, and for how long, in line with rules for SPII, Law Enforcement Sensitive, and FOUO information
- Define pilot success criteria with Government Product Owners and AI engineers, and keep each pilot's scope tied to those criteria from selection through go/no-go
- Prepare go/no-go decision packages from validation results, user testing, and cost, with a clear recommendation to move a pilot to sustainment, rework it, or stop
- Measure and report the value of each pilot, including time saved, quality of results, cost, and adoption, in a form OIG leadership can use for enterprise investment decisions
- Capture lessons learned and feed them into the next wave of candidates and into data platform priorities, so the data sets onboarded first are the ones the highest-value questions depend on
- Coordinate with the Scrum Master, AI engineers, UX researchers, and technical writer so discovery, build, testing, and documentation move in step, and maintain pilot risks, issues, and decisions
- Explain AI capabilities and limits in plain language to non-technical leaders and staff, and provide inputs to biweekly status briefings and monthly progress reports
Qualifications:
Required –
- Bachelor's degree in a relevant field + 10 years of experience in program or project management, business or data analysis, or technology portfolio management; equivalencies considered (Master's + 7 years; 16 years with no degree; AA + 12 years)
- U.S. citizenship and the ability to obtain and maintain a DHS Public Trust (High Risk) fitness determination
- Demonstrated experience leading discovery and requirements work with mission or business users, and turning their needs into prioritized, decision-ready proposals
- Experience managing pilots, proofs of concept, or a portfolio of analytics, AI, or data initiatives through selection, evaluation, and go/no-go decisions, including defining success criteria and measuring benefits
- Working knowledge of data governance and sensitive data handling (PII, SPII, Law Enforcement Sensitive) and of responsible AI practices such as output validation, human review, and documented limitations
- Strong written and oral communication, including briefing senior Government leaders and writing decision documents for non-technical readers
Desired –
- Experience in inspector general, audit, investigative, law enforcement, or other oversight environments
- Familiarity with Azure OpenAI, Azure AI Foundry, Databricks, or comparable enterprise AI and data platforms, and with generative AI uses such as document analysis, search, transcription, and translation
- PMP, PMI-CPMAI, or product ownership certification (CSPO or PSPO), and experience with Jira, Azure DevOps, or comparable tools
- Experience building benefit or adoption measures and presenting them in dashboards such as Power BI
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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:
$94,400 - $198,200 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 $94K-$198K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($146K) sits 31% below the category median. Disclosed range: $94K to $198K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
CACI International AI Hiring
CACI International has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, Ashburn, VA, US, Denver, CO, US. Compensation range: $172K - $218K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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