Agentic AI and Data Engineer

Booz Allen Hamilton; United States; On-site; USD 99,000 - 225,000; Full time

Job summary

Booz Allen Hamilton is seeking an experienced engineer to design and deliver production‑grade agentic AI systems that leverage prompting, RAG, and multi‑modal workflows. You will work with data engineers, architects, and product owners to build scalable AI solutions deployable across cloud and on‑prem environments.

You will prototype with modern GenAI stacks, ensure observability and safety, and drive eval‑driven development with a focus on measurable impact and responsible AI practices.

Qualifications

• 2+ years of software engineering experience in a professional setting.
• 2+ years of AI/ML focused roles in a professional setting.
• Proficiency with Python and object‑oriented programming for AI/ML solutions.
• Experience designing and delivering production‑grade generative or agentic AI applications.
• Experience with AI orchestration frameworks such as LangChain, agent workflows, and multi‑provider model integration.
• Experience with RAG architectures, eval methodologies, and asynchronous or event‑driven patterns.
• Knowledge of AI data processing for text, audio, and multi‑modal inputs.
• Ability to obtain a Secret clearance.
• Bachelor’s degree in CS or Engineering.

Responsibilities

• Design adaptable agentic AI architectures that support multiple model providers and tool ecosystems.
• Build modular components for prompting, retrieval, orchestration, tool execution, memory, and evaluation.
• Integrate LLMs, embeddings, RAG pipelines, and memory mechanisms into production systems.
• Apply advanced prompting techniques and orchestration patterns to enable autonomous, observable AI workflows.
• Design and implement offline and online evaluation frameworks for safety and business impact.
• Optimize models and workflows for cost, latency, reliability, and scalability.
• Develop data pipelines for ingestion, cleaning, embedding, indexing, and refreshing data for RAG/memory.
• Create cross‑modal processing workflows and enable multi‑modal reasoning.

Skills

Software engineering

AI/ML in production

Python programming

Agentic AI design

Prompting & LLMs

Asynchronous/event-driven

System design

Education

Bachelor's degree in CS or Engineering

Tools

LangChain

Docker

Kubernetes

CI/CD tooling

AWS

Job description

The Opportunity

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 containerising, 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.

Qualifications

• 2+ years of experience with software 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 methodologies, 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 to 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 engagements.
• 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.

Clearance

Applicants selected will be subject to a security investigation and may need to meet eligibility requirements for access to classified information.

Compensation and Benefits

Salary range: $99,000.00 to $225,000.00 (annualized USD). This is one component of the total compensation package and may vary based on location, experience, and other factors.

Benefits include health, life, disability, financial, and retirement benefits, paid leave, professional development, tuition assistance, work‑life programs, and dependent care. Full‑time and part‑time employees working at least 20 hours a week are eligible for the benefit program, while those less than 20 hours are eligible for selected offerings excluding health.

All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.