AI/ML Engineering & Mission Integration

• Build and integrate AI/ML capabilities supporting mission workflows using FedRAMP-authorized services available in AWS GovCloud, from data preparation through deployment, evaluation, and monitoring.
• Design, develop, test, and operationalize AI/ML solutions aligned to defined mission and business requirements.
• Translate operational use cases into scalable AI/ML architectures, services, and integration patterns.
• Develop APIs, services, and application integrations that expose AI/ML capabilities to mission applications and enterprise platforms.
• Collaborate with application engineers, data engineers, cloud engineers, cybersecurity teams, and mission stakeholders throughout the solution lifecycle.
• Conduct technical evaluations and proof-of-concept implementations to determine whether proposed AI/ML technologies are appropriate for the mission, security environment, and available GovCloud services.
• Maintain technical documentation covering architecture, model behavior, interfaces, dependencies, deployment procedures, and operational requirements.

Python & AI/ML Development

• Develop production-quality AI/ML applications and supporting services using Python.
• Build reusable Python modules, services, utilities, and automation supporting data processing, inference, evaluation, and system integration.
• Apply standard software engineering practices including source control, automated testing, code review, dependency management, and CI/CD.
• Develop and integrate machine learning models, foundation models, or AI services based on approved use cases and architecture.
• Optimize AI/ML application performance, reliability, scalability, and resource utilization.
• Troubleshoot issues involving model behavior, data quality, application integration, cloud services, and runtime environments.

Generative AI & Retrieval-Augmented Generation

• • Design and implement Generative AI and Retrieval-Augmented Generation (RAG) patterns when included within the approved solution scope.
  • Develop workflows for document ingestion, parsing, chunking, embedding generation, indexing, retrieval, prompt construction, and model inference.
  • Integrate approved large language models and foundation-model services with enterprise applications and mission data sources.
  • Evaluate retrieval quality, response relevance, groundedness, hallucination risk, and overall solution effectiveness.
  • Develop prompt-management, model-routing, and orchestration patterns where appropriate.
  • Implement safeguards and validation mechanisms to reduce the risk of inappropriate, inaccurate, or unauthorized model outputs.
  • Support secure integration of vector stores, search services, knowledge repositories, and other components required by RAG architectures.