Active Public Trust clearance is required for this role.

Full Stack AI Engineer

This role blends hands-on software engineering, applied AI, cloud-native development, and integration of generative AI accelerators into mission systems.

Job Description:
• Design, develop, and deploy AI-enabled full stack applications that support document processing, intelligent search, generative AI use cases, and agentic workflows.
• Implement and integrate reusable AI accelerators such as RAG pipelines, model orchestration layers, hybrid search components, and structured data generation capabilities.
• Develop secure, cloud-native backend APIs and microservices (preferably AWS-based), data ingestion, and workflow automation.
• Collaborate with data scientists to train, fine-tune, or evaluate machine learning and LLM models implemented within client systems.
• Apply LLMOps best practices for model lifecycle, experimentation, telemetry, CI/CD, testing, and monitoring.
• Ensure solutions comply with client security requirements, federal accessibility standards, and responsible AI principles.
• Support rapid prototyping as well as hardening prototypes to production-grade systems aligned to client modernization initiatives (e.g., intelligent document management, NLP-driven search, cloud migration).
• Participate in sprint ceremonies, backlog refinement, and joint design sessions with product owners and technical leads.
• Produce documentation, architecture diagrams, and deployment artifacts.

Required Qualifications:
• Experience building full stack applications using frameworks such as React, Angular, or Vue; and backend frameworks such as Node.js, Python FastAPI, or Java Spring.
• Hands-on experience applying machine learning or LLM capabilities in production or near-production environments.
• Strong Python skills for AI workflows and API development.
• Experience with AWS services (e.g., Lambda, S3, ECS/EKS, API Gateway, CloudFormation/Terraform).
• Familiarity with vector search, embeddings, RAG architectures, or NLP/LLM-driven systems.
• Experience with DevOps tooling (GitLab/GitHub CI/CD, Docker, Kubernetes).
• Ability to work directly with clients, refine requirements, and deliver iterative prototypes quickly.