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Senior AI Engineer (AWS)
General Dynamics Information Technology
3 hours ago
Full-time
On-site
Senior AI Engineer (AWS)
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 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.