Senior AI Software Engineer

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

1. 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.

2. 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.

3. 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.

4. Containerization & Platform Modernization

• Support the migration of DS Chat components from traditional deployments into containerized services.
• Develop, configure, and maintain container images using Podman or Docker.
• Create and maintain Podman Compose configurations defining application services, networking, volumes, and environment variables.
• Integrate services such as OpenWebUI, vLLM, Docling, PostgreSQL, pgvector, PgBouncer, and Redis.
• Develop scripts and automation for repeatable installation, deployment, upgrades, and recovery.
• Implement health checks, readiness checks, logging, and service dependency management.
• Troubleshoot container networking, authentication, storage, GPU access, and service connectivity.
• Collaborate with infrastructure and cybersecurity teams to improve deployment consistency and security.

5. Agentic AI & Future Capabilities

• Research, prototype, and develop agentic AI capabilities for future DS Chat releases.
• Implement controlled tool-calling and multi-step AI workflows.
• Develop integrations that allow approved AI models to interact with authorized enterprise APIs and services.
• Explore emerging technologies involving AI orchestration, workflow automation, and Model Context Protocol (MCP).
• Design secure execution boundaries, permissions, audit logging, and approval mechanisms for AI-driven actions.
• Collaborate with stakeholders to evaluate new AI capabilities for mission relevance, security, and operational feasibility.
• Implement safeguards to support human oversight and prevent unauthorized actions by AI-enabled systems.

6. Software Engineering, Testing & DevSecOps

• Apply software engineering best practices, including modular architecture, code reviews, documentation, and version control.
• Develop automated unit, integration, regression, and performance tests using frameworks such as pytest.
• Build integration tests validating communication between OpenWebUI, inference services, document processing components, and databases.
• Support CI/CD processes appropriate for disconnected and classified environments.
• Troubleshoot production issues through application logs, metrics, traces, and system diagnostics.
• Integrate operational logging and monitoring capabilities with tools such as Splunk.
• Assist with vulnerability remediation, dependency management, software supply chain reviews, and container security assessments.
• Produce technical documentation, deployment procedures, architecture diagrams, and operational runbooks.
• Participate in Agile development activities, including sprint planning, technical design reviews, and demonstrations.

7. Security & Classified Environment Support

• Design software solutions suitable for secure, on-premises, air-gapped government environments.
• Implement secure authentication integration, role-based access controls, API authorization, and data protection measures.
• Ensure AI services communicate only through authorized internal network paths.
• Support software packaging and offline deployment using preapproved dependencies, container images, and model artifacts.
• Collaborate with cybersecurity personnel to address security findings and support Risk Management Framework (RMF) activities.
• Assist with technical documentation and evidence required for TRB/CCB reviews and Authority to Operate (ATO) processes.
• Follow approved procedures for handling sensitive and classified information.

Required Qualifications

• Minimum Secret Clearance, no sponsorship
• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Computer Engineering, or a related technical field, or equivalent practical experience.
• 5+ years of professional software engineering experience, including backend application development.
• Strong proficiency in Python and experience developing production-quality software.
• Experience developing RESTful APIs and integrating distributed application services.
• Hands-on experience integrating LLMs or generative AI capabilities into software applications.
• Understanding of transformer-based models, inference APIs, prompt engineering, and LLM application development.
• Experience with RAG concepts, including document ingestion, embeddings, vector search, and retrieval pipelines.
• Experience working with relational databases such as PostgreSQL.
• Familiarity with container technologies such as Podman or Docker.
• Experience with Linux-based development and application deployment.
• Proficiency with Git, automated testing, debugging, and software development best practices.
• Experience troubleshooting applications across multiple interconnected services.
• Strong analytical, problem-solving, communication, and technical documentation skills.
• Ability to work effectively with cross-functional teams and government stakeholders.

Preferred Qualifications

• Experience with OpenWebUI, including configuration, integration, custom Functions, Tools, or application extensions.
• Hands-on experience with vLLM, Ollama, Hugging Face Transformers, or other LLM inference technologies.
• Experience serving or integrating open-weight LLMs in on-premises environments.
• Experience with NVIDIA GPUs, CUDA, GPU memory management, and AI inference optimization.
• Experience with PostgreSQL pgvector, vector similarity search, and semantic retrieval.
• Familiarity with embedding models, rerankers, and cross-encoder architectures.
• Experience with Docling, Tesseract, or document intelligence technologies.
• Experience implementing production RAG pipelines and evaluation frameworks.
• Familiarity with agentic AI frameworks, tool calling, and Model Context Protocol (MCP).
• Experience developing or maintaining containerized microservice architectures.
• Familiarity with Redis, PgBouncer, and asynchronous background processing.
• Experience with Red Hat Enterprise Linux and Windows Server.
• Experience with FastAPI, Pydantic, pytest, and related Python technologies.
• Experience using Splunk for application monitoring and troubleshooting.
• Familiarity with software supply chain security, vulnerability scanning, and container hardening.
• Experience supporting federal government systems, classified networks, or air-gapped deployments.
• Knowledge of federal cybersecurity requirements, RMF, and ATO processes.

The likely salary range for this position is $128,039 - $173,229. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possibly contractual requirements and could fall outside of this range.

Scheduled Weekly Hours:

40

Travel Required:

Less than 10%

Telecommuting Options:

Onsite

Work Location:

USA VA Sterling

Additional Work Locations:

Total Rewards at GDIT:

Our benefits package for all US-based employees includes a variety of medical plan options, some with Health Savings Accounts, dental plan options, a vision plan, and a 401(k) plan offering the ability to contribute both pre and post-tax dollars up to the IRS annual limits and receive a company match. To encourage work/life balance, GDIT offers employees full flex work weeks where possible and a variety of paid time off plans, including vacation, sick and personal time, holidays, paid parental, military, bereavement and jury duty leave. To ensure our employees are able to protect their income, other offerings such as short and long-term disability benefits, life, accidental death and dismemberment, personal accident, critical illness and business travel and accident insurance are provided or available. We regularly review our Total Rewards package to ensure our offerings are competitive and reflect what our employees have told us they value most.

 

 

Our Identity Verification Process:

As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during virtual interviews. We reserve the right to take your picture to verify your identity and prevent fraud. By proceeding, you authorize the collection, processing, and use of your biometric data for identity verification and security purposes.

 

 

About Our Work:

We are GDIT. A global technology and professional services company that delivers technology solutions and mission services to every major agency across the U.S. government, defense and intelligence community. Our 26,000 experts extract the power of technology to create immediate value and deliver solutions at the edge of innovation. We operate across 50+ countries worldwide, offering leading mission-ready capabilities in AI, cloud, cyber and software development.

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Equal Opportunity Employer / Individuals with Disabilities / Protected Veterans