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About This Role
Job ID
324312
Job Title: Staff AI/ML Engineering Manager
Job Category: Science
Time Type: Full time
Minimum Clearance Required to Start: TS/SCI
Employee Type: Regular
Percentage of Travel Required: Up to 10%
Type of Travel: Local
Anticipated Posting End: 8/31/2026The Opportunity:
This position is for an AI/ML Engineering Manager. We are seeking a talented and motivated AI/ML Engineering Manager to join our growing team. This is a unique player/coach role designed for an experienced AI/ML engineer who is passionate about both researching, building, developing AI/ML applications and leading people. You will be responsible for managing and mentoring a team of skilled AI/ML researchers and engineers while also actively apply cutting\-edge AI/ML algorithms in a variety of domains to meet the mission needs of our customers. You'll split your time effectively between guiding your team's success and rolling up your sleeves to wrangle data, write code, build and train models, solve complex technical challenges, and contribute directly to our AI/ML portfolio.
Responsibilities:
- Hands\-on AI/ML Development: Actively participate in the end\-to\-end machine learning lifecycle, contributing high\-quality, well\-tested, and maintainable code for data pipelines, model training, evaluation, and deployment to key AI/ML projects using our tech stack.
+ Proven proficiency in Python, including experience with key machine learning libraries (e.g., TensorFlow, PyTorch, Scikit\-learn, Pandas, NumPy).
- ML System Design \& Architecture: Contribute to technical design discussions and architectural decisions for scalable and robust machine learning systems, including data pipelines, model training infrastructure, serving layers, and MLOps frameworks.
- Code \& Model Quality: Actively participate in code reviews, ensuring adherence to coding standards, MLOps best practices, and high\-quality engineering principles, specifically for machine learning models and infrastructure (e.g., reproducibility, testability, explainability).
- Stay Current and Mentor: Keep abreast of cutting\-edge machine learning research, algorithms, MLOps tools, and cloud AI/ML services, advocating for their strategic adoption. Leverage this continuous learning to mentor and provide technical direction to your AI/ML team.
- Lead \& Mentor: Manage, coach, and mentor a team of AI/ML engineers, fostering their technical and professional growth, by providing career advice and helping with program technical guidance. Additionally, you will work with the greater AI/ML engineering group to cultivate a positive, collaborative, inclusive, and high\-performing team that is focused on bringing modern AI/ML development practices and robust engineering principles across all ARKA programs.
- Performance Management: Conduct regular 1:1 bi\-weekly meetings with your team, provide feedback on both technical and non\-technical topics, assist with setting clear goals, and manage performance reviews for your direct reports.
- Collaboration: Work closely with AI/ML engineering organization and other engineering leadership to align priorities, define requirements, and ensure successful project delivery across programs.
- Project Oversight: Help manage project priorities, timelines, and deliverables for your team, identifying and removing roadblocks.
- Hiring \& Onboarding: Participate in the recruitment, interviewing, onboarding, and retention of engineering talent for your team and the broader organization. Additionally works closely with engineering leadership to align current and new staff skillsets with program needs over time.
Qualifications:
*Required:*
- Bachelor’s degree in computer science, data science, mathematics, engineering, or a related field
- 3\+ years of experience in a formal or informal leadership capacity (e.g., Tech Lead, Team Lead, mentoring junior engineers, project leadership)
- 8\+ years of experience developing AI/ML applications, data science, or algorithm development
- Experience with Python and data science / machine learning libraries (e.g. PyTorch, TensorFlow, Keras, OpenCV, NumPy, Pandas, Polars, scikit\-learn, etc.)
- Experience with one or more of the following areas:
+ Applying unsupervised and/or supervised machine learning techniques
+ Applying and/or developing algorithms based in statistical analysis
+ Analyzing large datasets and building models to perform inference
- Applying Large Language Models (LLMs) and techniques such as retrieval augmented generation (RAG), fine tuning, and prompt engineering
- Experience with deep learning architectures (e.g. FCNs, CNNs, RNNs, Transformers, GANs)
- Experience with modern software development methodologies (Agile, Scrum, Kanban)
- Experience with version control systems such as Git and the associated tooling to for modern software version control
- Excellent communication, interpersonal, and collaboration skills
- Strong problem\-solving and analytical abilities
- A genuine passion for both technology and people leadership
- Ability to effectively balance management responsibilities with individual technical contributions
- Active TS/SCI U.S. Government Security Clearance
*Desired:*
- MS or PhD in machine learning, computer science, mathematics, or related fields
- Experience directly managing AI/ML engineers, including performance management cycles
- Experience with any of the following AI/ML domains:
+ Large Language Models and experience identifying ways to incorporate them into new areas and applications
+ Applying Transformer\-based architectures to domains in other areas outside of Natural Language Processing (NLP) such as computer vision
+ Object detection algorithms such as YOLO and Faster\-RCNN
+ Natural Language Processing algorithms such as BERT
+ Generative Adversarial Networks and Variational Autoencoders
+ Reinforcement learning and familiarity with Gymnasium Gym, RLlib, and Stable Baselines
+ Applying clustering algorithms and/or deep neural networks to real life problems
+ Implementing tracking and pattern\-of\-life algorithms
- Experience with Machine Learning libraries and frameworks such as HuggingFace and LangChain
- Experience with Computer Vision libraries such as OpenCV, Nerfstudio, FiftyOne, etc.
- Experience with Linux
- Familiarity with using AWS cloud computing resources such as EC2, S3, Lambda, etc.
- Experience with any of the following additional languages: Java, C\+\+, Rust, Go, and/or C\#
- Experience implementing algorithms on the GPU in Python or C\+\+ using CUDA and other CUDA libraries
- Experience in application deployment, virtualization, and containerization (e.g. Podman, Docker, Kubernetes, Rancher)
- Experience working with various Remote Sensing datasets (e.g. EO/OPIR/SAR images, passive RF, etc.)
- Experience shaping and writing proposals
*
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:
$108,400 \- 227,500 USD*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 $108K-$227K range is below the median for AI Engineering Manager roles in our dataset (median: $270K across 27 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 26,159 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At CACI International, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $293,500 based on 28 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($167K) sits 43% below the category median. Disclosed range: $108K to $227K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Architect ($292,900) and AI Safety ($274,200). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
CACI International AI Hiring
CACI International has 26 open AI roles right now. They're hiring across AI/ML Engineer, Prompt Engineer, AI Software Engineer, LLM Engineer. Positions span Remote, US, Ashburn, VA, US, Denver, CO, US. Compensation range: $79K - $252K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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