AI/ML Solutions Architect

System One
McLean, United States of America
yesterday

Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

McLean, United States of America

Tech stack

Artificial Intelligence
Amazon Web Services (AWS)
Code Generation
Information Engineering
Relational Databases
Database Queries
Decision Support Systems
DevOps
Elasticsearch
Graph Database
Python
PostgreSQL
Machine Learning
NLTK
NumPy
OpenCV
Oracle Applications
Query Optimization
Cloud Services
TensorFlow
Search Technologies
Software Engineering
Systems Architecture
Systems Integration
Unstructured Data
Workflow Management Systems
PyTorch
Large Language Models
Concurrency
Prompt Engineering
Deep Learning
Boto3
Generative AI
Pandas
Scikit Learn
Information Technology
Virtual Agents
Data Pipelines
Devsecops

Job description

  • 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 and 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.
  • Perform prompt engineering, safety patterns, and defensive design.
  • Integrate knowledge graphs and develop graph-enhanced retrieval solutions.
  • Create and fine-tune embeddings and LLMs to enhance domain performance, accuracy, and robustness.
  • Develop entity graphs using entity resolution techniques to enable graph analytics and improved retrieval.
  • Collaborate across engineering, security, and stakeholder teams to prototype rapidly, iterate responsibly, and deliver mission-ready outcomes.
  • Lead deployment in AWS cloud environments utilizing Infrastructure-as-Code, DevOps/DevSecOps, and operational excellence practices.
  • Own and drive the technical foundation and delivery process for mission AI solutions, including system architecture, tooling, engineering and delivery standards, and hands-on technical leadership.

Requirements

  • 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.
  • 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 LangChain, Haystack, crewAI, or similar.
  • Strong knowledge of core Python ML/data libraries: NumPy, Pandas, Scikit-learn, NLTK, OpenCV.
  • Familiarity with deep learning frameworks such as PyTorch or TensorFlow.
  • Experience with search technologies such as Elasticsearch or OpenSearch.
  • Experience with relational databases (PostgreSQL, Oracle) and in-memory analytics engines (DuckDB).
  • Experience using cloud SDKs (e.g., Boto3) and building reliable integrations with cloud services.
  • Familiarity with agentic AI frameworks such as AWS Strands Agents, PydanticAI, and related orchestration patterns.
  • Advanced prompt engineering skills for complex reasoning workflows beyond code generation.
  • Experience with asynchronous Python development (asyncio, concurrency, reliability).
  • Experience with MCP servers and tool-calling within agentic workflows (tool governance, security considerations).

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