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About This Role
Job Title: AI Engineering Lead Job Category: Information Technology Time Type: Full time Minimum Clearance Required to Start: None Employee Type: Regular Percentage of Travel Required: None Type of Travel: None
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The Opportunity:
CACI is seeking a visionary AI Engineering Lead to support the Department of Homeland Security (DHS) Office of the Inspector General (OIG). This role offers a unique opportunity to provide technical leadership and strategic direction for an enterprise AI transformation that strengthens national security oversight through investigative, audit, and inspection operations.
As the AI Engineering Lead, you will architect and oversee the implementation of AI capabilities across the DHS OIG enterprise, from OIGChat—a large language model assistant—to mission-specific investigative analytics applications. You will lead technical teams, define AI architecture patterns, establish model governance frameworks, and ensure responsible AI practices across all implementations. You will coordinate access to Azure OpenAI and Azure AI Foundry services, implement cost controls and guardrails, and provide expert guidance on complex AI problem sets from prototype through production. Working within secure government cloud environments, you will shape the technical vision and execution strategy for AI capabilities on a transformational program of national importance. Join us to make a meaningful impact by leading the future of AI-enabled federal oversight.
Responsibilities:
Provide technical leadership and architectural direction for all AI capabilities, including OIGChat, pilot applications, and enterprise AI platform services across Azure Government and Commercial environments
Oversee secure, governed access to Government-approved AI models (Azure OpenAI, Azure AI Foundry, commercial LLM providers), including model selection, routing architecture, API management, and integration patterns
Lead design, development, and architecture of mission-specific AI applications for investigative analytics, hotline analysis, eDiscovery, and oversight use cases from concept through production
Establish and enforce model governance, cost management, and responsible AI frameworks, including usage monitoring, token consumption tracking, dashboards, thresholds, alerts, and guardrails to prevent unauthorized use, excessive cost, and data leakage
Define and implement AI/ML architecture patterns that ensure all model access consumes governed, authoritative datasets from the enterprise data platform in compliance with data governance, classification, and quality policies
Lead solution architecture and prototyping efforts, providing expert guidance on complex AI problem sets, technical feasibility assessments, and level-of-effort estimates
Coordinate AI workload requirements and scalability planning across infrastructure, data, and application layers to support current and future AI use cases
Lead and coordinate AI engineering and data science resources, including surge support for specialized technical analysis, model development, validation, and production deployment
Support model auditing, explainability, bias detection, and compliance with federal AI requirements and DHS OIG data governance policies
Collaborate with Government leadership, platform engineers, security specialists, and product teams to align AI capabilities with mission priorities, Agile delivery processes, and enterprise architecture standards.
Qualifications:
Required –
Bachelor's degree + 15 years of experience in artificial intelligence, machine learning, software engineering, or related field; equivalencies considered (Master's + 12 years; 21 years with no degree; AA + 17 years)
Proven technical leadership experience architecting and delivering enterprise-scale AI/ML solutions, with demonstrated ability to lead engineering teams and coordinate cross-functional efforts
Deep expertise in large language models (LLMs), generative AI, Azure OpenAI/Azure AI services, and enterprise AI platforms with understanding of model governance, responsible AI, and production ML operations
Extensive experience designing AI architectures, including RAG (Retrieval-Augmented Generation), model routing, API patterns, guardrails, monitoring, cost optimization, and integration with enterprise data platforms
Strong background in AI/ML frameworks (LangChain, Semantic Kernel, TensorFlow, PyTorch), Python development, and cloud-native AI services with ability to provide hands-on technical guidance
Desired –
Experience leading AI initiatives in federal government, law enforcement, investigative, or national security environments with understanding of sensitive data handling, ATO processes, and compliance requirements
Hands-on experience with MLOps, model lifecycle management, AI governance frameworks, and production AI monitoring and observability tools
Background in investigative analytics, fraud detection, text analytics, NLP, or mission-driven AI applications supporting oversight, audit, or law enforcement functions
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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.
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:
$114,600-$252,100 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 $114K-$252K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($183K) sits 14% below the category median. Disclosed range: $114K to $252K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
CACI International AI Hiring
CACI International has 10 open AI roles right now. They're hiring across AI/ML Engineer, Prompt Engineer, Data Scientist. Positions span King of Prussia, PA, US, Denver, CO, US, Ashburn, VA, US. Compensation range: $75K - $252K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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