Full Stack Developer, Associate

Job title: Full Stack Developer, Associate

Location: Bethesda, MD

Clearance: Public Trust

Sponsorship: No sponsorship assistance is available for this position.

Duration: 7 Months (May 2026 - December 2026)

Hybrid: Minimum of 2 Days Onsite (May increase as Client needs may increase)

LCG is seeking a Full Stack Developer / AI Engineer
- Associate to support our NIH client in developing innovative AI-powered solutions using Azure OpenAI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern full stack technologies.

This role will support an NIH client that aims to design and implement AI-driven applications that automate and enhance internal NIH business processes. The developer will design and build Generative AI applications, chatbots, and intelligent automation tools to support use cases such as compliance review, policy analysis, meeting scheduling, grant monitoring, and research reporting.

The successful candidate will support configuration and assist with optimizing secure Azure OpenAI cloud infrastructure, design LLM-integrated applications using Python-based APIs, and enhance the existing client AI Chat Tool to improve knowledge retrieval and operational efficiency. The role involves building React-based front-end interfaces, developing FastAPI services for AI integration, and implementing vector databases to support semantic search and RAG pipelines.

This role will work closely with client leadership, technical teams, and pilot users to prototype, deploy, and refine AI capabilities while ensuring alignment with federal IT security, governance, and change management processes.

This position offers an opportunity to contribute to cutting-edge AI modernization initiatives at NIH, helping federal programs leverage Generative AI technologies to improve efficiency, decision-making, and operational insights.

• Develop and implement AI-powered applications using Azure OpenAI, LLM technologies, Retrieval-Augmented Generation (RAG) pipelines, and vector database architectures
• Design and build Generative AI applications, intelligent agents, and chatbot solutions that automate internal business processes and support staff workflows.
• Implement semantic search and document retrieval systems using vector databases to support AI-driven knowledge retrieval.
• Enhance and maintain the existing client AI Chat Tool, improving user experience and response accuracy through AI technologies.
• Develop intelligent Generative AI applications supporting use cases such as:
• Compliance verification for new policies and funding opportunities
• Policy and regulatory change analysis
• AI-driven meeting scheduling and coordination
• Monitoring of grant and clinical trial activities
• Knowledge retrieval from internal documentation and SOP repositories

• Support the configuration and enhancement of secure Azure cloud infrastructure used to host AI applications and services, including:
  • Azure OpenAI services
  • Azure Storage accounts
  • Azure Applications and Database services
• Assist cloud and infrastructure teams with deploying AI-powered applications that leverage vector databases and RAG architectures.
• Work within the existing Azure OpenAI environment to integrate AI services and ensure applications function effectively within the client's cloud infrastructure.
• Collaborate with cloud engineering and security teams to ensure AI solutions align with NIH cloud governance, security policies, and infrastructure standards.
• Assist with documenting AI solution architecture and implementation components.

• Develop full stack AI applications using React for front-end interfaces and Python-based APIs for backend services.
• Build RESTful APIs and AI service endpoints using FastAPI to connect LLM services with enterprise applications.
• Support development of RAG pipeline components integrating vector databases with enterprise data sources.
• Assist in developing LLM-integrated applications and APIs that connect AI services with enterprise systems.
• Implement data pipelines and integrations using SQL, No
  
  SQL, and vector databases as…