Interested in this AI/ML Engineer role at MANTECH?
Apply Now →Skills & Technologies
About This Role
MANTECH seeks a motivated, career and customer\-oriented Senior AI ML Engineer. This is currently a hybrid position with two to three days onsite in Ashburn, VA.
In this role, you will collaborate within a cross\-functional team to develop new Artificial Intelligence/Machine Learning (AI/ML) based solutions into operational pipelines to deliver mission impact for U.S. Customs and Border Protection (CBP). The ideal candidate will have deep expertise and experience with predictive modeling lifecycles, hands\-on experience with machine learning tools and frameworks, and a pragmatic, customer\-centric approach to applying ML models to solve complex problems.
Each day CBP oversees the massive flow of people, capital, and products that enter and depart the United States via air, land, sea, and cyberspace. The volume and complexity of both physical and virtual border crossings require the application of solutions to aid officers in detecting threats while promoting efficient trade and travel.
Responsibilities include but are not limited to:
- Lead the integration and deployment of trained AI/ML models into production environments (e.g., cloud, edge devices) using MLOps best practices.
- Develop and optimize model training \& inference pipelines for real\-time execution and efficiently handle large\-scale data processing.
- Work with data science teams to structure automated ML model health monitoring and refresh capabilities.
- Implement continuous integration, delivery and training (CI/CD/CT) workflows with commercial and open\-source modeling platforms/services.
- Coordinate with Data Science and Engineering teams to build scalable feature stores for optimal model training \& execution workflows.
- Research, evaluate and recommend new tools, applications, software packages for MLOps engineering that can be adopted and approved for use in the CBP environment.
- Collaborate with cross\-functional teams (e.g., Software Engineering, Data Science) to integrate and test multiple candidate AI/ML models and applications for operational assessment.
Required Qualifications:
- HS Diploma/GED and 15\-20 years of experience, AS/AA and 13\-18 years, BS/BA and 7\+ years or MS/MA/MBA and 5\+ years or PhD/Doctorate and 3\+ years.
- Hands\-on experience with LLMs such as Gemini, Llama, Mistral, or other open\-source and commercial models. Experience with LLM application frameworks such as LangChain, LlamaIndex, or equivalent custom frameworks. Ability to optimize LLM systems for latency, throughput, scalability, reliability, GPU utilization, and inference cost. Experience deploying machine learning or LLM services in AWS, Azure, or Google Cloud. Demonstrated experience designing and deploying LLM solutions, including the following:
+ Retrieval\-augmented generation (RAG)
+ Agentic workflows and tool calling
+ Prompt engineering and structured outputs
+ Model fine\-tuning, e.g. LoRA
+ Embedding\-based search and semantic retrieval
- Strong understanding of transformer architectures, tokenization, embeddings, context windows, inference parameters, and common LLM failure modes. Experience evaluating LLM applications for accuracy, relevance, hallucination, safety, latency, and cost.
- Experience with vector databases or search technologies such as OpenSearch, Elasticsearch, Milvus, Qdrant, Pinecone, Weaviate, or pgvector.
- Experience designing and integrating RESTful APIs and microservices using frameworks such as FastAPI.
- Working knowledge of SQL and experience with relational, document, or NoSQL databases.
- Familiarity with Docker, Kubernetes, CI/CD pipelines, monitoring, logging, and production incident troubleshooting.
Preferred Qualifications
- Master’s degree or Ph.D. in Computer Science, Machine Learning, Natural Language Processing, or a related discipline.
- Experience training, fine\-tuning, quantizing, or serving open\-source LLMs using tools such as PyTorch, Ollama, or TensorRT\-LLM.
- Understanding of AI security risks, including prompt injection, data leakage, unsafe tool execution, model abuse, and adversarial inputs. Experience building multi\-agent systems, multimodal applications, long\-context workflows, or human\-in\-the\-loop AI systems.
- Knowledge of advanced retrieval techniques, including hybrid search, reranking, query expansion, metadata filtering, and retrieval evaluation.
- Experience in LLM projects from initial requirements and proof of concept through production deployment and ongoing optimization.
- Strong knowledge of software engineering practices, including version control, code review, automated testing, system design, and technical documentation.
Clearance Requirements:
- Must be a U.S. Citizen and be able to obtain and maintain a CBP suitability prior to start this position.
- The ability to obtain and maintain a Top\-Secret clearance.
Physical Requirements:
- The person in this position needs to occasionally move about inside the office to access file cabinets, office machinery, or to communicate with co\-workers, management, and customers, which may involve delivering presentations,
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MANTECH, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
MANTECH AI Hiring
MANTECH has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Ashburn, VA, US, Remote, US, Herndon, VA, US.
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 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 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).
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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.