Senior AI Software Engineer Job Sterling area,Alaska USA,Software Development

Type of

Requisition :
Pipeline Clearance Level Must Currently Possess:
Secret Clearance Level Must Be Able to Obtain:
Top Secret Public Trust/Other

Required:

None Job Family:
Data Science and Data Engineering

Job Qualifications:

Skills:

Agile Software Development, Back-End Development, Microservices Architecture

Certifications:

None

Experience:

7 + years of related experience US Citizenship

Required:

Yes

Job Description:

GDIT is seeking a highly motivated and experienced Senior AI Software Engineer to support the development, modernization, integration, and sustainment of our on-premises, AI-powered enterprise chatbot platform supporting the U.S. Department of State in Sterling, VA. The selected candidate will design, develop, and integrate software solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, document processing technologies, and modern AI inference frameworks.

This role requires a strong foundation in software engineering, Python development, API integration, containerization, and AI application architecture. The engineer will help transition existing AI services toward a scalable, containerized architecture while introducing new capabilities that enhance system performance, reliability, security, and user experience. The ideal candidate is a hands-on software engineer who can work across the AI application stack, from backend development and model serving integration to data processing, automated testing, and deployment.

The individual will collaborate with AI/ML engineers, data engineers, infrastructure specialists, cybersecurity teams, and government stakeholders to deliver secure, reliable AI capabilities in highly controlled, classified, and air-gapped environments.

Key Responsibilities

AI Application Development & Integration Design, develop, test, and maintain Python-based applications, microservices, and APIs supporting an on prem chatbot. Extend, configure, and integrate OpenWebUI with enterprise AI services and backend systems. Develop custom OpenWebUI Functions, Tools, and integrations to support mission-specific use cases. Integrate approved LLMs into chatbot using OpenAI-compatible APIs and inference frameworks such as vLLM and Ollama. Develop reusable software components for AI model access, prompt management, response processing, and application workflows.

Collaborate with AI/ML engineers to improve model inference performance, response quality, and application reliability. Support multimodal AI use cases, including document understanding, optical character recognition, and vision-language model integration. Develop backend capabilities to support secure enterprise AI workflows.

Retrieval-Augmented Generation (RAG) & Document Processing Design, implement, and optimize RAG workflows for enterprise document search and question answering. Integrate document processing technologies such as Docling, Tesseract, and vision-language models into automated ingestion pipelines. Develop document chunking, metadata extraction, embedding generation, and retrieval workflows. Integrate embedding models and reranking models to improve retrieval accuracy and relevance. Work with PostgreSQL and pgvector to support vector search, semantic retrieval, and document indexing.

Develop APIs and background processing services for document ingestion and knowledge base management. Collaborate with data engineers to ensure accurate storage, indexing, and retrieval of enterprise information. Implement testing and evaluation approaches to measure RAG accuracy, retrieval relevance, and answer quality.

AI Model Serving & Inference Integration Integrate and maintain AI inference services using vLLM, Ollama, and compatible model-serving frameworks. Support the deployment and configuration of approved open-weight LLMs, embedding models, rerankers, and vision-language models. Develop software integrations using OpenAI-compatible REST APIs. Assist in optimizing inference configurations, including context length, GPU memory utilization, concurrency, and model serving performance. Support the transition from existing inference frameworks to improved architectures as technologies mature.

Collaborate with AI/ML engineers to troubleshoot inference errors, model loading issues, and integration failures. Develop benchmarking and performance-testing utilities for AI workloads.

Containerization & Platform Modernization Support the migration of DS Chat components from traditional deployments into…