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About This Role
Job ID
330022
Job Title: Staff AI/ML Engineer (Large Language Model)
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
\* \* \*The Opportunity:
The Staff AI/ML Engineer (LLMs) will lead the development of Agentic AI capabilities and other LLM based capabilities for a multitude of mission management applications.
Responsibilities:
Lead and mentor a multidisciplined team consisting of developers and researchers to implement machine learning algorithms to solve a broad set of challenges for our various customers
- Lead and mentor a multidisciplinary team delivering advanced AI/ML solutions
- Apply LLMs to complex domain\-specific problems and operational workflows
- Adapt and fine\-tune foundation models for specialized use cases
- Design and implement retrieval\-augmented generation (RAG) systems and semantic search architectures
- Build production\-grade LLM applications and agentic systems
- Deploy scalable AI solutions across cloud, on\-prem, and hybrid environments
- Analyze large, multi\-modal datasets to extract meaningful features and actionable insights
- Translate emerging research into applied, mission\-relevant capabilities
- Communicate technical strategy, status, and risks to internal and external leadership
Qualifications:
*Required:*
- Active TS/SCI U.S. Government Security Clearance
- B.S. in machine learning, computer science, mathematics, or related fields
- 8\+ years of experience, preferably in software development or as a data scientist with 2\+ years of building LLM applications using some of the following:
+ Fine\-tuning foundational models
+ Steering Techniques (e.g Sparse auto encoders, representation tuning)
+ Building adapters to use foundational models (e.g. PEFT, llama factory)
+ Prompt engineering techniques / Inference time techniques (e.g. chain of thought, tree of thoughts, etc.)
+ Using Retrieval Augmented Generation techniques to populate and query vector databases (e.g. Weaviate, pinecone, pgvector)
+ Using LLM Frameworks (e.g. LangChain, DSPy, Microsoft Agent Framework)
+ Using AI APIs ( e.g AWS Bedrock, OpenAI)
+ Using LLM deployment frameworks (eg llama.cpp, vllm, tgi)
+ Developing UIs with ReAct
- Experience leading an interdisciplinary team of researchers and software developers and working with a program manager to define project scope and schedule to ensure we meet project milestones as defined by our customers
- Experience with Python and data science / machine learning libraries (e.g. NumPy, Pandas, Polars, scikit\-learn, etc.)
- Experience contributing on a team using version control (e.g. git, GitLab, Bitbucket)
*Desired:*
- M.S. or PhD in machine learning, computer science, mathematics, or related fields
- Experience leading an interdisciplinary team of researchers and software developers
- Experience with any of the following:
+ Large Language Models and experience identifying ways to incorporate them into new domains and applications
+ Applying Transformer\-based architectures to domains in other areas outside of Natural Language Processing (NLP) such as computer vision
+ Natural Language Processing algorithms such as BERT
+ Reinforcement learning and familiarity with Gymnasium Gym, OpenEnv, TorchRL, 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 GenAI Ops techniques (e.g. LLM\-as\-a\-judge) and frameworks (e.g. LangFuse, MLFlow, Arize Phoenix)
+ Experience with Machine Learning libraries and frameworks such as HuggingFace and LangChain
+ Experience with Linux
+ Experience with CUDA and Python libraries such as CuPy, Numba, CuSignal, CuDF, etc.
+ 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 in application deployment, virtualization, and containerization (e.g. Podman, Docker, Kubernetes, Rancher)
- Experience shaping and writing proposals
- Adjudicated Counter Intelligence or Full Scope Polygraph
*
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 above the median for LLM Engineer roles in our dataset (median: $156K across 8 roles with salary data).
View full LLM Engineer salary data →Role Details
About This Role
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 4,317 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At CACI International, this role fits into their broader AI and engineering organization.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
Skills Required
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Compensation Benchmarks
LLM Engineer roles pay a median of $200,500 based on 18 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($167K) sits 16% below the category median. Disclosed range: $108K to $227K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
CACI International AI Hiring
CACI International has 4 open AI roles right now. They're hiring across Research Engineer, AI/ML Engineer, LLM Engineer. Positions span Florham Park, NJ, US, Washington, DC, US, Remote, US. Compensation range: $172K - $252K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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