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About This Role
An Iron EagleX Opportunity
Iron EagleX, a GDIT company, contributes to the U.S. government’s mission of protecting our nation and enables our customers to make quicker decisions and act faster than our adversaries.
Clearance Level
Top Secret/SCI
Category
Data Science and Data Engineering
Location
Arlington, Virginia
*(Onsite Workplace)*
Key Skills For Success
AI Agents
AI Ops
AI Systems
CI/CD
Machine Learning (ML)
##### REQ\#:RQ223221
##### Public Trust:None
##### Requisition Type:Regular
##### Your Impact
Own your opportunity to support our nation's defense. Make an impact by connecting and securing critical operations across the globe, keeping our country safe and secure.
Job Description
-------------------
YOUR IMPACT
Own your opportunity to work with the largest government agency in the nation. Make an impact by advancing the Department of War’s mission to keep our country safe and secure.
OUR COMPANY
Iron EagleX (IEX), a wholly owned subsidiary of General Dynamics Information Technology (GDIT), delivers agile IT and Intelligence solutions. Combining small\-team flexibility with global scale, IEX leverages emerging technologies to provide innovative, user\-focused solutions that empower organizations and end users to operate smarter, faster, and more securely in dynamic environments.
JOB DESCRIPTION
Iron EagleX is seeking a Senior Agentic AI/ML Engineer to support our AI team in Crystal City, VA. This role will design, develop, deploy, and sustain machine learning, artificial intelligence, and agentic AI capabilities that support advanced analytics, automation, and mission\-focused decision\-making. The AI/ML Engineer will work across the full AI development lifecycle, including data preparation, model development, agentic workflow design, evaluation, integration, deployment, and operational sustainment.
MEANINGFUL WORK AND PERSONAL IMPACT
As a Senior Agentic AI/ML Engineer, you will be a core member of the AI team responsible for building practical, reliable, and scalable AI, machine learning, and agentic AI solutions. You will help transform complex data, emerging AI concepts, and mission needs into production\-ready capabilities that improve workflows, enhance analytics, automate complex tasks, and support mission outcomes.
JOB DUTIES (INCLUDE BUT ARE NOT LIMITED TO)
- Design, develop, train, evaluate, and deploy machine learning models to support mission and business use cases.
- Develop clean, maintainable, and efficient Python code while adhering to best practices and coding standards.
- Build and maintain AI/ML pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.
- Design, develop, and integrate modern AI model capabilities into applications and workflows, including prompting, tool calling, RAG, structured outputs, and agentic patterns.
- Develop and support agentic AI workflows that can reason over tasks, use tools, retrieve relevant information, execute multi\-step processes, and interact with external systems under defined controls and guardrails.
- Evaluate agentic systems for accuracy, reliability, safety, task completion, tool\-use effectiveness, latency, and operational suitability.
- Support the development of AI\-enabled applications, analytics tools, automation capabilities, and decision\-support systems.
- Collaborate with data engineers, software engineers, analysts, and stakeholders to define requirements and deliver effective AI/ML and agentic AI solutions.
- Design, develop, and integrate APIs, services, and external tools to enable AI/ML and agentic capabilities within larger software systems.
- Establish and maintain reproducible environments, CI/CD workflows, and container\-based deployments using tools such as Docker.
- Work with structured and unstructured data sources to ensure data quality, integrity, usability, and performance.
- Implement and maintain version control using Git to streamline collaboration and code management.
- Ensure all developed solutions meet high standards for security, quality, reliability, explainability, and maintainability.
- Contribute to all stages of the AI/ML, agentic AI, and software development life cycles, from concept and experimentation through testing, deployment, monitoring, and sustainment.
REQUIRED SKILLS
- Proficiency in Python and commonly used AI/ML libraries and frameworks.
- Hands\-on experience developing, training, evaluating, and deploying machine learning models.
- Strong understanding of supervised learning, unsupervised learning, model evaluation, feature engineering, and data preprocessing techniques.
- Experience working with structured and/or unstructured data in support of analytics or AI/ML use cases.
- Familiarity with modern AI and language model concepts, including prompting, embeddings, retrieval\-augmented generation, structured outputs, tool calling, and model evaluation.
- Experience designing, developing, or integrating agentic AI workflows or applications using LLMs, tools, APIs, memory, retrieval, and multi\-step task execution.
- Understanding of agentic AI concepts, including task planning, goal decomposition, tool selection, orchestration, human\-in\-the\-loop review, guardrails, and evaluation of agent behavior.
- Experience designing or supporting AI/ML pipelines and reproducible development environments.
- Understanding of MLOps concepts, including model versioning, experiment tracking, model registry, CI/CD, monitoring, and deployment of ML\-enabled systems.
- Familiarity with LLMOps or agent operations concepts, including prompt/version management, evaluation datasets, observability, traceability, safety controls, and monitoring of deployed AI systems.
- Familiarity with RESTful APIs and integrating AI/ML or agentic AI capabilities into software applications, workflows, or enterprise systems.
- Proficiency with Git\-based version control and collaborative software development workflows.
- Familiarity with containerization tools such as Docker.
- Ability to work effectively both independently and collaboratively in a fast\-paced environment.
- Strong problem\-solving skills and the ability to translate complex technical concepts into practical, secure, and mission\-relevant solutions.
DESIRED SKILLS
- Experience with PyTorch, TensorFlow, scikit\-learn, MCP, Google ADK, A2A, LangChain, LangGraph, or similar AI/ML and agentic AI frameworks and tools.
- Experience building or supporting LLM\-powered applications, including chat\-based interfaces, agentic workflows, tool calling, retrieval\-augmented generation, workflow automation, or multi\-agent systems.
- Experience designing agents that interact with external tools, APIs, databases, knowledge bases, or enterprise systems.
- Familiarity with agentic design patterns such as planner\-executor, router, evaluator, reflection, tool\-using agents, multi\-agent collaboration, and human\-in\-the\-loop escalation.
- Familiarity with vector databases, semantic search, embeddings, and knowledge retrieval systems.
- Experience evaluating LLM and agentic AI systems using automated evaluations, benchmark datasets, human review, red teaming, or operational performance metrics.
- Experience with cloud\-native AI/ML development or deployment environments.
- Experience with Docker and Kubernetes for scalable deployment of AI/ML and agentic AI workloads.
- Familiarity with data engineering concepts, including ETL workflows, data validation, and data pipeline orchestration.
- Experience with relational databases such as PostgreSQL.
- Experience with DevSecOps practices, secure software delivery, or mission\-focused software environments.
- Familiarity with AI safety, security controls, data protection, and governance considerations for deployed AI and agentic systems.
WHAT YOU’LL NEED TO SUCCEED
- Clearance: Current TS/SCI Clearance with current or willingness to obtain CI polygraph
- Experience:10\+ years of related experience
- Education: Bachelor’s degree in Computer Science, Software Engineering, or a related field (or equivalent experience)
- Role requirements: Work is onsite in Crystal City, VA with optional CONUS travel
- Due to US Government Contract Requirements, only US Citizens are eligible for this role
GDIT IS YOUR PLACE
At GDIT, the mission is our purpose, and our people are at the center of everything we do.
- Growth: AI\-powered career tool that identifies career steps and learning opportunities
- Support: An internal mobility team focused on helping you achieve your career goals
- Rewards: Comprehensive benefits and wellness packages, 401K with company match, competitive pay and paid time off
- Community: Award\-winning culture of innovation and a military\-friendly workplace
OWN YOUR OPPORTUNITY
Explore a career at GDIT and you’ll find endless opportunities to grow alongside colleagues who share your passion for the mission and delivering results. \#iexjobs \#iexpriority
Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans
\#iexjobs
### Work Requirements
Years of Experience
10 \+ years of related experience
- may vary based on technical training, certification(s), *or* degree
Certification
Travel Required
10\-25%
Citizenship
U.S. Citizenship Required
### Salary and Benefit Information
The likely salary range for this position is $170,000 \- $230,000\. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.
### Our Identity Verification Process
As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.
### About Our Work
We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50\+ countries worldwide, offering leading mission\-ready capabilities in AI, cloud, cyber and software development.
Join our Talent Community to stay up to date on our career opportunities and events at gdit.com/tc.
*Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans*
Salary Context
This $170K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At General Dynamics Information Technology, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($200K) sits 9% below the category median. Disclosed range: $170K to $230K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span Bethesda, MD, US, Springfield, VA, US, Fort Bragg, NC, US. Compensation range: $164K - $304K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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