Cloud Solutions Architect

Booz Allen Hamilton • Fayetteville, NC

2h • $99,000 - $225,000

About The Position

The Cloud Solutions Architect designs, develops, and delivers production-grade generative and agentic AI systems that demonstrate practical value and create measurable impact. The role combines engineering expertise with product skills to build AI applications using prompting, retrieval augmented generation (RAG), agentic orchestration, evaluation pipelines, and human-in-the-loop systems.

The architect creates modular, reusable AI application patterns and integrates cloud-hosted, local, and hybrid model providers. Solutions use capabilities such as structured prompting, tool use, workflow orchestration, and multimodal reasoning, and are designed for deployment across cloud platforms and portable, self-contained builds. The role optimizes for latency, cost efficiency, observability, and safety, and uses AI-assisted development tools alongside evaluation-driven development and continuous experimentation.

The position helps 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. The architect works with AI and ML engineers, data engineers, data scientists, solutions architects, product owners, and clients to identify opportunities, define requirements, deliver solutions, and support adoption. TS/SCI clearance is required. The position is based in Fayetteville, North Carolina, and is not listed as remote.

Requirements

• At least 2 years of software engineering experience.
• At least 2 years of professional experience in AI- or ML-focused roles.
• Experience with an object-oriented programming language such as Python and applying it to AI or ML solution development.
• Experience designing and implementing production-grade generative or agentic AI applications.
• Experience with RAG architectures, evaluation methodologies, experimentation workflows, and asynchronous or event-driven programming patterns.
• Knowledge of AI data-processing techniques, including text, audio, and multimodal data.
• TS/SCI clearance is required.
• Bachelor’s degree.

Nice To Haves

• 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 through tool-calling or computer-use patterns.
• Experience with modern front-end libraries and frameworks for component-based UI development, including React, and workflows such as build pipelines, automated testing, and code-quality tooling.
• AWS ML, Data Engineer, or Solutions Architect certification.

Responsibilities

• Design adaptable AI architectures supporting multiple model providers, tool ecosystems, modalities, and deployment modes.
• Integrate LLMs, embeddings, RAG pipelines, structured outputs, and long-context or memory mechanisms into production-ready systems.
• Develop data pipelines for ingestion, cleaning, chunking, embedding, indexing, and continuous refresh of structured and unstructured data for RAG and memory systems.
• Incorporate guardrails, safety filters, access controls, and monitoring mechanisms for responsible and secure deployment of agentic AI systems.
• Deploy AI services securely and at scale on AWS or equivalent cloud platforms.
• Apply CI/CD and evaluation-driven development practices, including automated testing of prompts and workflows, versioning prompts and agents, and safe rollout of model updates.
• 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 technical and non-technical stakeholders.
• Lead workshops and prototyping sessions to accelerate adoption.
• Provide guidance on responsible AI practices, ethics, and compliance.
• Select and combine prompting strategies, RAG architectures, agentic workflows, and fine-tuned or foundation models for mission-critical systems.

Benefits

• Health benefits
• Life insurance
• Disability benefits
• Financial benefits
• Retirement benefits
• Paid leave
• Professional development
• Tuition assistance
• Work-life programs
• Dependent care
• Recognition awards program

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level