Senior AI Engineer (AWS)
About The Position
GDIT is seeking a Senior AI Engineer to design, build, deploy, and evolve secure, scalable, and reliable AWS AI solutions supporting generative AI and scientific research for the NIAID research community. The role calls for hands-on expertise across the AWS AI stack, including Amazon Bedrock, Bedrock AgentCore, Amazon SageMaker, and Amazon Q, alongside knowledge of modern application architecture, AI/LLM technologies, infrastructure, networking, identity, security, automation, and DevOps. As a senior member of the Cloud Platform & DevOps team, the engineer serves as an AI engineering expert and works with application developers, AI engineers, cloud engineers, platform developers, security teams, customers, and other stakeholders to translate requirements into well-designed solutions. The role includes customer-facing technical advice, mentoring, and ownership of the reliability, quality, and performance of production AI agents. Remote candidates may be considered, though working onsite in Rockville two to three days per week is strongly preferred. The position requires obtaining and maintaining a Public Trust and completing onsite fingerprinting at a designated facility.
Requirements
- Bachelor’s degree in Computer Science, AI/Data Science, Engineering, or equivalent practical experience.
- At least 8 years of relevant experience.
- Proven experience designing, building, and operating production AI agents or generative-AI applications, beyond prototypes or notebooks.
- Hands-on experience with Amazon Bedrock, Bedrock AgentCore, Amazon SageMaker, and Amazon Q.
- Knowledge of 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 using Terraform, CloudFormation, or AWS CDK, and CI/CD pipelines for AI/ML workloads.
- Deep hands-on AWS experience across compute, networking, storage, identity, security, monitoring, and APIs, including designing scalable, resilient, secure architectures.
- Experience with DevOps/CI-CD practices, Docker containerization, and operational support.
- Strong architecture, troubleshooting, consulting, and communication skills, including the ability to independently own complex solutions and collaborate across technical and non-technical stakeholders.
- Strong Python proficiency and software engineering practices, including version control, testing, and code review.
- Ability to obtain and maintain a Public Trust.
Nice To Haves
- Experience with the AWS Well-Architected Framework and GenAI reference architectures or reusable accelerators.
- Experience with LLMOps/MLOps tooling for model evaluation, observability, prompt management, or guardrails.
- Experience fine-tuning or customizing foundation models; knowledge of knowledge bases and vector stores such as Amazon OpenSearch, Aurora pgvector, or Amazon Kendra.
- Relevant AWS certifications, such as AWS Solutions Architect – Professional or Generative AI Developer – Professional.
- Experience supporting scientific research, federal government, healthcare, or similarly regulated environments.
Responsibilities
- Design, build, and ship production autonomous and multi-agent systems using Amazon Bedrock and Bedrock AgentCore, including 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; and apply prompt engineering and model customization.
- Design highly available, scalable, and secure AWS AI architectures using the Well-Architected Framework and generative-AI best practices; contribute to AI strategy, reference architectures, and reusable accelerators.
- Operationalize AI agents end to end, including automated evaluation, guardrails, observability, cost and latency optimization, CI/CD, and Infrastructure as Code; own deployed agents’ reliability, quality, and performance.
- Implement responsible-AI guardrails, data privacy, and security controls, including Amazon Bedrock Guardrails, IAM, encryption, and PII handling, and support compliance with relevant frameworks.
- Communicate with technical and non-technical stakeholders, mentor junior engineers, and stay current with AWS AI developments.
- Architect, implement, and evolve AWS cloud solutions for generative-AI application architectures, including multimodal LLMs, 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 for production GenAI applications.
- Serve as a senior technical resource and customer-facing advisor, mentoring engineers, communicating tradeoffs, and presenting recommended AI approaches.
Benefits
- Medical plan options, including plans with Health Savings Accounts
- Dental plan options
- Vision plan
- 401(k) plan with company match
- Full flex work weeks where possible
- Paid vacation, sick, and personal time
- Paid holidays
- Paid parental leave
- Military leave
- Bereavement leave
- Jury duty leave
- Paid Family Leave of up to 160 hours in a rolling 12-month period for eligible employees
- Short- and long-term disability benefits
- Life insurance
- Accidental death and dismemberment insurance
- Personal accident insurance
- Critical illness insurance
- Business travel and accident insurance
- Wellness programs
- Internal mobility support and career-development tools
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What This Job Offers
Job Type
Full-time
Career Level
Senior
