AI​/ML Solutions Architect; Agentic Systems

Job Family
:

Travel Required
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Clearance Required
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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.
• Lead AI initiatives spanning:
  • 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, Dev Ops/Dev Sec Ops , and operational excellence patterns.
• You will own and drive the technical foundation and delivery rigor for mission AI solutions:
  • 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:

• Must be able to OBTAIN and MAINTAIN a Federal or DoD "PUBLIC TRUST"; candidates must obtain approved adjudication of their PUBLIC TRUST prior to onboarding with Guidehouse. Candidates with an ACTIVE PUBLIC TRUST or SUITABILITY are preferred.
• Bachelor’s degree in Engineering, IT, Computer Science, or related field (or equivalent experience).
• Minimum EIGHT (8) years in solutions architecture, software engineering, data engineering, and/or applied ML with a track record of delivering production systems
  . A Master’s degree may be substituted for up to 2 years of relevant professional experience.
• Strong Python proficiency and strong SQL skills (data modeling, query optimization).
• Experience designing and delivering cloud-based AI/ML solutions end-to-end (ingestion → modeling → deployment → monitoring) in secure environments.
• Hands-on experience with AI application frameworks such as Lang Chain, Haystack, crewAI
  , or similar.
• Strong knowledge of core Python ML/data libraries:
  Num Py, Pandas, Scikit-learn, NLTK, OpenCV
  .
• Familiarity with deep learning frameworks such as Py Torch or Tensor Flow
  .
• Experience with search technologies such as Elasticsearch or Open Search
  .
• Experience with relational databases (e.g.,
  Postgre
  
  SQL, Oracle
  ) and in-memory analytics engines (e.g.,
  DuckDB
  ).
• Experience using cloud SDKs…