Senior AI/RAG Engineer - Remote, Secret Clearance

HII

United States

Remote

USD 160,000 - 175,000

Full time

7 days ago

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Job summary

HII’s Mission Technologies division seeks an AI/RAG Engineer to design, build, and optimize secure generative AI, retrieval-augmented generation, agentic, and AI-governance capabilities for an enterprise AWS GovCloud IL4/IL5 environment.

The role leads the AI intelligence layer including data ingestion, retrieval, model integration, prompting, agent workflows, evaluation, AI gateway configuration, and AI security, collaborating with cyber, DoW identity, frontend, and backend teams to deliver

Qualifications

Strong Python development experience.
Experience building and operating production generative AI, RAG, or AI-enabled applications.
Strong understanding of large language models, embeddings, vector search, retrieval patterns, prompt design, structured outputs, and AI service APIs.
Experience designing RAG pipelines and integrating models with enterprise data sources, tools, APIs, or application services.
Experience developing or evaluating agentic workflows that use models, retrieval, and approved tools to complete bounded tasks.
Hands-on experience with Amazon Bedrock, Boto3, and AWS cloud services.
Experience evaluating and improving AI capabilities for quality, groundedness, latency, reliability, security, and cost.
Experience implementing practical AI security and governance controls, including prompt-injection mitigation, input and output validation, access-control integration, rate limiting, usage monitoring, logging, and data protection.
Experience with Git-based development, automated testing, and collaborative software-delivery practices.
Clearance: Must possess and maintain an active Secret clearance at the time of consideration.

Responsibilities

Design, develop, and optimize production generative AI, RAG, and agentic capabilities using approved Amazon Bedrock services.
Evaluate and integrate foundation models based on quality, relevance, latency, reliability, security, and cost.
Build and improve RAG pipelines supporting ingestion, preprocessing, chunking, embeddings, vector search, retrieval, reranking, and context generation.
Design agentic workflows that combine foundation models, retrieved knowledge, approved tools, and enterprise APIs to complete bounded multi-step tasks.
Develop prompts, structured-output patterns, tool or function-calling interfaces, and guardrail configurations appropriate to application requirements.
Establish AI evaluation methods, test datasets, and monitoring approaches to assess response quality, groundedness, accuracy, safety, tool-use outcomes, latency, reliability, and cost.
Develop knowledge-ingestion pipelines for structured and unstructured data while preserving source-level access controls and applicable data-handling requirements.
Implement AI security controls, including prompt-injection defenses, input and output validation, data protection, audit logging, and human-review or escalation workflows where required.
Design and administer AI gateway configurations for approved model and agent access, including model routing and allowlists, service-identity or virtual-key controls, rate limits, budgets, usage attribution, fallback behavior, logging, and guardrail integration.
Develop Python-based AI services, APIs, and integration patterns for consumption by backend application services.
Partner with platform teams on infrastructure-as-code, deployment, observability, and operational support for AI/RAG services, AI gateways, ingestion pipelines, vector-search capabilities, and supporting AWS resources.
Collaborate with backend, front-end, cybersecurity, platform, and identity teams to integrate secure and dependable AI capabilities into production applications.