Job Family :
Travel Required :
Clearance Required :
We are seeking an AI/ML Solutions Architect (Agentic Systems) to design and deliver secure, scalable, and cost-effective cloud-native AI solutions for federal clients. You will bridge complex mission needs and modern technology by owning end-to-end architectures and leading hands-on implementation—especially for agentic AI systems, RAG-based applications, and production-grade ML pipelines .
This role blends technical vision + practical delivery leadership : selecting tools, defining architectures, establishing engineering standards, and guiding implementation through prototypes, code reviews, and reference solutions.
What You Will Do:
- Design and implement agentic AI systems that enable autonomous decision-making, workflow orchestration, and mission process optimization—with appropriate guardrails and human oversight.
- Develop Generative AI applications for summarization, extraction, predictive insights, and conversational interfaces.
- Build and maintain scalable data pipelines integrating structured + unstructured data to support analytics and AI workloads.
- Apply advanced statistical and machine learning techniques to decision support and policy/program evaluation.
- Retrieval-Augmented Generation (RAG) and evaluation
- Re-ranking strategies and retrieval quality optimization
- Prompt engineering, safety patterns, and defensive design
- Knowledge graph integration and graph-enhanced retrieval
- AI chatbots and conversational agents
- Fine-tune embeddings and LLMs (when appropriate) to improve domain performance, accuracy, robustness, and retrieval quality.
- Build entity graphs using entity resolution (matching, deduplication, linking, relationship discovery) to enable graph analytics and enhanced retrieval.
- Collaborate across engineering, security, and stakeholders to prototype rapidly , iterate responsibly, and deliver mission-ready outcomes.
- Lead deployment in AWS-first cloud environments , leveraging Infrastructure-as-Code, DevOps/DevSecOps, and operational excellence patterns.
- End-to-end solution architecture : system boundaries, trust zones, data flows, integrations/APIs, security controls, observability, and cost models.
- Tooling and platform selection : LLMs, embeddings, vector stores, orchestration frameworks, graph technologies, data platforms—documenting tradeoffs and decisions.
- Engineering and delivery standards : secure SDLC, CI/CD quality gates, automated testing, code review practices, evaluation harnesses, and production readiness checklists.
- Hands-on technical leadership : prototypes, reference implementations, PR reviews, mentoring, and architecture governance to ensure delivery quality.
What You Will Need: