AI Platform and Applications Engineer

National Endowment for Dem•Washington, DC

8h•Hybrid

About The Position

The National Endowment for Democracy (NED) supports freedom around the world and is dedicated to strengthening democratic institutions worldwide. NED is seeking talented, entrepreneurial professionals motivated by purpose and committed to excellence to join their dynamic team. The AI Platform and Applications Engineer will design, build, deploy, and support secure AI-powered applications and core platform services in NED’s local-first AI environment. This role focuses on translating approved business use cases into reliable internal tools and workflows, while also maintaining the application-layer components needed to run those solutions effectively. The position combines hands-on software engineering with practical platform stewardship, including model access and routing, retrieval pipelines, application integration, testing, monitoring, and operational support. The Engineer will collaborate with the IT team and other internal partners on security, governance, infrastructure, and procurement, aiming to deliver useful AI applications, improve platform reliability, and help NED adopt AI capabilities safely and in alignment with its mission. This is a hybrid position based in Washington, DC.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent professional experience
8–10 years of experience in software engineering, platform engineering, solutions engineering, or a related technical field
At least 2–3 years of hands-on experience building applications with modern AI/LLM technologies
Strong programming skills in Python and/or TypeScript/Node.js
Experience building and maintaining APIs, backend services, and production-grade integrations
Experience developing AI-enabled applications using LLMs, embeddings, retrieval patterns, and structured outputs
Hands-on experience with RAG pipelines and agentic workflows including document processing, chunking, embeddings, vector search, relevance tuning, and grounding strategies
Experience with model access layers or inference runtimes such as vLLM, TGI, llama.cpp, Hugging Face Transformers, or similar tools
Familiarity with containerized deployment and modern engineering operations, including Git, Docker, CI/CD, and application monitoring
Experience operating and supporting technical services in a Linux-based environment
Working knowledge of application observability, performance tuning, troubleshooting, and production support
Experience writing technical documentation and communicating effectively with both technical and non-technical stakeholders
Ability to work collaboratively in a mission-driven environment and translate ambiguous needs into practical solutions

Nice To Haves

Experience working in local, on-premises, private cloud, or otherwise restricted computing environments
Familiarity with Kubernetes or similar orchestration platforms
Experience with enterprise search, OpenSearch/Elasticsearch, vector databases, or other retrieval infrastructure
Familiarity with frontend frameworks for lightweight internal tools and dashboards
Exposure to evaluation frameworks, red-teaming approaches, or quality measurement for AI systems
Experience supporting multimodal or structured extraction use cases
Familiarity with authentication and identity integration patterns such as SSO and SAML
Experience working with Security, data governance, compliance, and procurement partners in enterprise technology delivery
Familiarity with governance, policy, vendor review, or procurement processes related to enterprise AI tooling
Experience in nonprofit, public sector, international affairs, or other mission-driven organizations

Responsibilities

Translate approved organizational needs into practical AI-enabled applications, copilots, automations, and APIs with clear success criteria
Design, develop, test, and maintain internal AI applications that integrate with approved systems, documents, and knowledge sources
Build user-facing and system-facing components, including APIs, lightweight interfaces, workflow integrations, and background services
Develop retrieval-augmented generation (RAG), agentic, and related patterns for enterprise search, question answering, summarization, extraction, and other approved use cases
Use tool calling, structured outputs, and workflow orchestration where they improve reliability, traceability, and user outcomes
Administer and improve core AI application platform components in NED’s local secure AI environment, including model endpoints, inference routing, embeddings services, retrieval services, and supporting application runtimes
Configure and maintain development, test, and production deployment pipelines for AI applications using modern engineering practices
Manage packaging, containerization, release processes, version control, and environment promotion for platform services and applications
Support platform observability through logging, metrics, tracing, dashboards, and routine health checks
Monitor application and platform performance, troubleshoot issues, and optimize reliability, latency, and cost
Build and maintain ingestion and retrieval pipelines for approved internal content sources
Implement document processing, parsing, chunking, metadata enrichment, embeddings, indexing, and ranking strategies to improve answer quality and search performance
Maintain vector stores, document stores, and related retrieval components used by AI applications
Improve content freshness, retrieval relevance, grounding, and output consistency through testing and tuning
Establish and maintain testing practices for AI applications, including unit, integration, regression, and offline evaluation workflows
Define and track measures such as answer quality, groundedness, latency, reliability, adoption, and operational performance
Support incident triage, issue resolution, root-cause analysis, and service improvement for AI applications in production
Create and maintain technical documentation, runbooks, architecture diagrams, and support procedures
Partner with business teams to identify, prioritize, prototype, and productionize high-value AI use cases
Collaborate with IT and other internal stakeholders on hosting, access, identity, network, security, governance, and enterprise architecture requirements
Contribute technical input to decisions involving tools, platforms, and service providers that support AI delivery
Help develop internal guidance, usage patterns, training materials, and adoption support for AI-enabled tools and workflows