layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Senior AI Software Engineer at General Dynamics Information Technology

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.

Senior AI Software Engineer

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