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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Agentic AI and Data Engineer - **Company:** Booz Allen Hamilton Inc. - **Location:** Honolulu, HI, United States - **Experience:** Experienced - **Salary:** $99,000.0 - $225,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Software Quality, Continuous Integration, Cursor, Software Debugging, Memory Management, Event-Driven Programming, Design of User Interfaces, Information Extraction, Interoperability, Python (Programming Language), Object-Oriented Software Development, Performance Tuning, Productivity Software, Azure Machine Learning, Systems Integration, Unstructured Data, Management of Software Versions, Reinforcement Learning, Data Processing, Enterprise Software Applications, ReactJS, Evaluation Pipelines, Retrieval-Augmented Generation, Transfer Learning, Delivery Pipeline, Large Language Models, Multi-Agent Systems, Generative AI, Agentic-AI, SC Clearance, AI Platforms, Kubernetes, Low Latency, Virtual Agents, Asynchronous Programming, Data Pipelines, Serverless Computing, Human in the Loop, Docker, Agent2Agent Protocol - **Published:** October 28, 2026 - **Apply:** https://www.clearancejobs.com/jobs/9271340/agentic-ai-and-data-engineer ## About the Role * 2+ years of experience with sof tware engineering * 2+ years of experience in AI or ML-focused roles in a professional work environment * Experience with an object-oriented programming language such as Python, and applying it to AI / ML solution development * Experience designing and implementing production-grade generative or agentic AI applications * Experience with AI orchestration frameworks such as LangChain, agent workflows, tool integration, and multi-provider model integration * Experience with RAG architectures, evaluation met hodologies, experimentation workflows, and asynchronous or event-driven programming patterns * Knowledge of data processing techniques for AI, including text, audio, and multi-modal * Ability to obtain a Secret clearance * Bachelor's degree in a CS or Engineering field Nice If You Have: * Experience with agent frameworks, interoperability standards, and multi-agent patterns such as MCP, A2A, LangGraph, or equivalent * Experience with model fine-tuning, prompt tuning, domain adaptation, or reinforcement learning from human or AI feedback * Experience designing evaluation suites or safety testing frameworks for AI systems, and integrating AI systems with external tools, APIs, or enterprise systems via tool-calling or computer-use patterns * Experience delivering AI solutions in client-facing e nga gements * Experience with modern front-end libraries and frameworks for component-based UI development, including React, and with workflows such as build pipelines, automated testing, and code quality tooling * Experience with in-browser or edge AI execution and performance optimization techniques, as well as modern build and packaging approaches for portable or offline-capable applications * Experience with developer productivity tools such as Cursor and Windsurf * Secret clearance * Master's degree in CS, AI, or a related field * AWS Machine Learning, Data Engineer, or Solutions Architect Certification ## Description As an experienced engineer, you know how to design, develop, and deliver production-grade agentic AI systems that demonstrate the practical value of generative AI, large language models ( LLMs ) , and autonomous workflows. This role combines deep technical expertise with strong product skills to design AI applications that leverage prompting, retrieval-augmented generation ( RAG ) , agentic orchestration, evaluation pipelines, and human-in-the-loop systems to deliver measurable impact. You will architect modular, reusable AI application patterns, integrate multiple model providers such as cloud-hosted, local, and hybrid , and apply modern GenAI stack capabilities, including structured prompting, tool use, workflow orchestration, and multi-modal reasoning. You will design solutions deployable across various contexts, from cloud-hosted platforms to portable, self-contained builds, optimizing for latency, cost efficiency, observability, and safety. You will rapidly prototype and iterate using AI-assisted development tools, validating hypotheses through eval-driven development and continuous experimentation. In this role, you'll define the direction of mission-critical agentic systems by selecting and combining prompting strategies, RAG architectures, agentic workflows, and fine-tuned or foundation models as appropriate. You'll be part of a large community of AI and ML engineers across the company, collaborating with data engineers, data scientists, solutions architects, and product owners to deliver world-class solutions. What You'll Do: * Design adaptable agentic AI architectures that support multiple model providers, tool ecosystems, modalities, and deployment modes. * Build modular and reusable components for prompting, retrieval, orchestration, tool execution, memory management, and evaluation to enable rapid development of new AI capabilities. * Integrate LLMs, embeddings, RAG pipelines, structured outputs, and long-context or memory mechanisms into production-ready systems. * Apply advanced prompting techniques such as few-shot, chain-of-thought, tool-calling, and function-calling, orchestration frameworks such as LangChain or equivalent , and agentic architectures such as MCP, A2A, or similar patterns, to enable goal-directed autonomy with guardrails, observability, and human oversight, including planning, tool use, delegation, and recovery from failure. * Design and implement evaluation frameworks, both offline and online, to measure correctness, robustness, safety, and business impact of AI systems. * Optimize models and workflows for cost, latency, reliability, and scalability, using systematic benchmarking and experimentation. * Develop data pipelines for ingestion, cleaning, chunking, embedding, indexing, and continuous refresh of structured and unstructured data for RAG and memory systems. * Combine text, audio, vision, and other modalities in unified processing workflows, including document understanding, transcription, summarization, and cross-modal reasoning. * Leverage vector databases, hybrid search, reranking, and retrieval optimization techniques to enhance grounding and reduce hallucination in RAG systems. * Incorporate guardrails, safety filters, access controls, and monitoring mechanisms to ensure responsible and secure deployment of agentic AI systems. * Deploy AI services securely and at scale on AWS or equivalent cloud platforms. * Use containerizing, including in Docker or Kubernetes, or serverless approaches for flexible deployment. * Apply CI / CD and eval-driven development best practices for AI systems, including automated testing of prompts and workflows, versioning of prompts and agents, and safe rollout of model updates. * Use asynchronous programming and event-driven patterns to support scalable, long-running, or multi-agent workflows. * Leverage modern build and packaging workflows to deliver optimized, portable application artifacts. * Use AI assistance tools to accelerate development, debugging, and system design while maintaining engineering rigor and code quality. * Collaborate with clients to identify high-value AI opportunities and define solution requirements. * Present AI capabilities and technical solutions to both technical and non-technical stakeholders. * Lead workshops and prototyping sessions to accelerate adoption. * Provide guidance on responsible AI practices, ethics, and compliance.