Interested in this AI/ML Engineer role at General Dynamics Information Technology?
Apply Now →Skills & Technologies
About This Role
Clearance Level None Category Data Science and Data Engineering Location Remote, Working from the USA (Hybrid Workplace)
Key Skills For Success AI Agents
AI Prompting
Amazon Bedrock
AWS Bedrock AgentCore
Generative AI
REQ#:RQ230524
Public Trust:NACLC (T3)
Requisition Type:Regular
Your Impact
Own your opportunity to be on the frontlines of health innovation. Deliver for America’s health agency missions and enhance lives for hundreds of millions every day.
Job Description
GDIT is looking for a Senior AI Engineer (AWS) to join our NIAID team, designing, building, and deploying AI solutions on AWS that serve the NIAID research community.
We are seeking a highly experienced and customer-focused engineer to architect, build, and evolve secure, scalable, and reliable AWS AI solutions supporting generative AI and scientific research. Our ideal candidate combines hands-on expertise across the AWS AI stack (Amazon Bedrock, Bedrock AgentCore, Amazon SageMaker, Amazon Q) with a strong command of modern application architecture, AI/LLM technologies, infrastructure, networking, identity, security, automation, and DevOps practices.
As a senior member of our Cloud Platform & DevOps team, you will serve as an AI engineering expert. You will work closely with application developers, AI engineers, cloud engineers, platform developers, security teams, customers, and other stakeholders to translate requirements into well-designed solutions.
Remote candidates will be considered; however, 2-3 days onsite in Rockville is strongly preferred.
This role requires you to obtain and maintain a Public Trust and to provide onsite fingerprinting at a designated facility. This investigation may review personal and criminal behavior, financial conduct, foreign influence, as well as other adjudications.
Responsibilities:
- Design, build, and ship autonomous and multi-agent systems in production using Amazon Bedrock and Bedrock AgentCore — covering tool use, orchestration, memory, identity, and human-in-the-loop patterns.
- Select, evaluate, and integrate foundation models on Amazon Bedrock; design, implement, and evaluate RAG pipelines (including retrieval quality and RAG evaluation); apply prompt engineering and model customization.
- Design highly available, scalable, secure AI architectures on AWS, applying the Well-Architected Framework and generative-AI best practices. Contribute to the overall AI strategy, reference architectures, and reusable accelerators.
- Operationalize AI agents end-to-end — automated agent evaluation, guardrails, observability, cost and latency optimization, CI/CD, and Infrastructure as Code (Terraform or CloudFormation). Own the reliability, quality, and performance of deployed agents.
- Implement responsible-AI guardrails, data privacy, and security controls (Amazon Bedrock Guardrails, IAM, encryption, PII handling) and ensure compliance with relevant frameworks.
- Communicate clearly with technical and non-technical stakeholders; mentor junior engineers; and stay current with the fast-moving AWS AI landscape.
- Architect, implement, and evolve AWS cloud solutions supporting generative AI application architectures, including Amazon Bedrock, multimodal LLMs, Retrieval-Augmented Generation (RAG), vector search, knowledge integration, agentic capabilities, and distributed cloud AI services.
- Partner with application developers, AI engineers, security teams, and stakeholders to translate requirements into scalable, well-architected cloud platform solutions.
- Engineer full-stack AWS solution plumbing to support production GenAI applications.
- Serve as a senior technical resource and customer-facing advisor — mentoring engineers, communicating tradeoffs, and presenting recommended AI approaches to technical and non-technical stakeholders.
Required Qualifications:
- Bachelors in Computer Science, AI/Data Science, Engineering (or equivalent practical experience)
- 8+ years of relevant experience.
- Proven experience designing, building, and running AI agents or generative AI applications in production — not only prototypes or notebooks.
- Hands-on experience with the AWS AI/ML stack: Amazon Bedrock, Bedrock AgentCore, Amazon SageMaker, and Amazon Q.
- Solid command of generative AI and agentic concepts: foundation models, prompt engineering, RAG and RAG evaluation, vector databases, tool/function calling, multi-agent orchestration, memory, and agent evaluation.
- Practical experience with at least one agent framework (Strands Agents, LangGraph, LangChain, CrewAI, or the Bedrock Agents SDK).
- Experience with Infrastructure as Code (Terraform, CloudFormation, or AWS CDK) and CI/CD pipelines for deploying AI/ML workloads.
- Deep hands-on AWS experience across compute, networking, storage, identity, security, monitoring, and APIs, with experience designing scalable, resilient, secure architectures.
- Experience with DevOps/CI-CD practices, containerization (Docker), and operational support.
- Strong architecture, troubleshooting, consulting, and communication skills, with demonstrated ability to independently own complex solutions and collaborate across technical and non-technical stakeholders.
- Strong Python proficiency with solid software engineering practices (version control, testing, code review).
- Ability to obtain and maintain a Public Trust.
Preferred Qualifications:
- Well-Architected Framework and GenAI reference-architecture / reusable-accelerator experience.
- LLMOps/MLOps tooling (model evaluation, observability, prompt management, guardrails).
- Fine-tuning or customizing foundation models; knowledge bases and vector stores (Amazon OpenSearch, Aurora pgvector, Amazon Kendra).
- Relevant AWS certifications (e.g., AWS Solutions Architect – Professional, Generative AI Developer – Professional).
Experience supporting scientific research, federal government, healthcare, or similarly regulated environments.
*
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: Full benefits, wellness programs, 401K matching, competitive salary, and paid time off.
- Community: Award-winning culture of innovation and a military-friendly workplace.
#GDITFedHealthJobs
Work Requirements
Years of Experience 8 + years of related experience
- may vary based on technical training, certification(s), or degree
Certification AWS Certified Generative AI Developer - Professional | Amazon Web Services (AWS) - Amazon Web Services (AWS)
Travel Required None
Salary and Benefit Information
The likely salary range for this position is $129,813 - $166,750. 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 $129K-$166K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($148K) sits 30% below the category median. Disclosed range: $129K to $166K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
General Dynamics Information Technology AI Hiring
General Dynamics Information Technology has 15 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect, Data Scientist. Positions span Vienna, VA, Remote, US, McLean, VA. Compensation range: $98K - $253K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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.