AI Engineer

Charity Navigator•Union City, NJ

5d•$105,000 - $125,000•Remote

About The Position

Charity Navigator is the world's largest and most-utilized independent nonprofit evaluator. The organization guides informed giving by evaluating the financial health, accountability, transparency, and impact of charities and provides data on over 245,000 nonprofits. As a 501(c)(3) public charity itself, Charity Navigator depends on support from individuals, corporations, and foundations to provide this trusted data free of charge to the public. You will build and ship production LLM-powered and agentic features that make 2.4 million nonprofit profiles easier to search, compare, and understand - so a donor gets a clear answer instead of a spreadsheet. This is application-layer work. You will partner with our existing engineering team rather than owning the pipelines yourself, so your time goes into the AI features people use. You will report to the VP of Engineering and work closely with them and other senior engineers on system design, technical direction, and strategic AI opportunities. You will own AI features from day one, with regular design review and hands-on guidance on the harder architectural calls, and take on more architectural ownership as you build depth in production AI systems.

Requirements

2-3+ years building or deploying machine learning models or AI-driven applications.
Bachelor’s degree in Computer Science, Engineering, AI/ML, or a related field, or a relevant certificate program. Demonstrated technical proficiency and relevant industry experience will be considered in lieu of a formal degree.
Python and ML fundamentals. 2+ years of Python, professional or substantial project-based experience, with a solid grasp of core machine learning concepts and API integration.
LLM and agentic systems. Hands-on experience with foundation model providers (Anthropic, OpenAI, and similar), prompt engineering, and agentic patterns - using orchestration frameworks such as LangChain or LlamaIndex, or integration standards such as the Model Context Protocol.
Retrieval and data integration. Practical experience querying and tuning a vector database (pgvector, OpenSearch, or similar) for RAG applications.

Nice To Haves

Managed cloud AI services, such as Amazon Bedrock, are preferred.
Cloud deployment of application code via serverless or containerized patterns, AWS required.
Scrum or other Agile development experience.
Cloud, Gen AI, or machine learning certifications, such as AWS Certified Generative AI Developer, Certified Machine Learning, or Solutions Architect.

Responsibilities

Build and ship production LLM-powered and agentic features - prompt orchestration, tool calling, embeddings, structured outputs - favoring reusable components over one-off solutions.
Build API Integrations, MCP servers, and retrieval-augmented generation (RAG) workflows that connect our existing data systems to vector search, so that AI features surface and explain our published ratings data accurately and cite the source of each figure.
Build and run evaluation infrastructure - prompt regression tests, LLM-as-a-judge evaluations, synthetic datasets - that catch feature quality regressions before users do.
Monitor AI features in production. Track accuracy, latency, token cost, and hallucination rates, then debug and tune prompts based on what you find.
Implement runtime safety and reliability guardrails that keep AI outputs within quality, compliance, and operational bounds.
Research and recommend orchestration frameworks, vector stores, and model providers, weighing cost and operational burden against speed. Final build-versus-buy calls are made jointly with the VP of Engineering.
Work directly with non-technical stakeholders to scope problems and give them honest assessments of what AI can and cannot do.
Evaluate emerging models and tools, use AI-assisted development extensively, and help raise team engineering standards through code review.