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About Givzey & Version2.aiJoin the Future of Fundraising at Givzey!Givzey is one of the fastest-growing and most innovative technology companies serving the nonprofit sector, on a mission to unlock more generosity through AI-powered donor engagement. At the center of that innovation is Version2.ai, the world’s first Autonomous AI fundraisers—Virtual Engagement Officers (VEOs)—designed to independently manage donor engagement and generate revenue. Unlike traditional AI tools that simply make staff more efficient, VEOs expand fundraising capacity by acting as AI workers that operate donor portfolios, build relationships, and secure gifts on their own. In just three years, Givzey’s platform has already helped organizations raise $10M+ through autonomous engagement, including individual gifts as large as $100,000. Alongside this breakthrough technology, Givzey’s Gift Agreement Platform modernizes the multi-year giving process, enabling nonprofits to secure, manage, and forecast commitments with unprecedented ease.About the RoleWe’re hiring an Applied AI Engineer to build production AI systems that real customers depend on.This role is for an experienced software engineer who also understands modern AI systems. You should be comfortable building with LLMs, agents, retrieval pipelines, and workflow orchestration, but just as comfortable thinking about system design, reliability, testing, deployment, debugging, and long-term maintainability.You’ll work on everything from agent workflows and retrieval systems to backend APIs, evaluation tooling, observability, and production infrastructure.We care a lot about engineering quality. That means building systems that are understandable, testable, observable, and reliable in production. We are looking for someone who can help raise the engineering bar around AI development and bring strong technical judgment to a fast-moving environment.What You’ll Work OnAgentic AI workflows that automate complex business processesAI-powered product experiences that combine LLMs, retrieval, backend systems, and human review workflowsRetrieval systems that connect AI agents to organization-specific knowledge and dataBackend services and APIs that allow AI systems to safely interact with internal product workflows and dataPrompting, evaluation, and observability systems that improve the quality and consistency of generated outputsMonitoring and debugging infrastructure for production AI systemsHuman-in-the-loop review systems that combine automation with expert oversightInternal AI tooling, orchestration frameworks, and operational infrastructureResponsibilitiesDesign, build, and maintain production-grade AI systems and customer-facing AI featuresDevelop agentic workflows using LLMs, retrieval systems, tools, APIs, and backend servicesBuild backend services, orchestration systems, automation, and infrastructure supporting AI-powered workflowsDesign and implement retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic retrieval, and context assemblyIntegrate foundation models through platforms such as Amazon Bedrock or Agent CoreDevelop robust prompting strategies, structured outputs, guardrails, and workflow logic for production use casesImplement evaluation systems for prompts, agents, and workflows, including regression testing, trace review, golden datasets, and human QA processesMonitor and improve production AI systems for quality, reliability, latency, observability, and cost efficiencyDebug AI behavior through logs, traces, evaluations, user feedback, and production telemetryCollaborate closely with engineering, product, operations, and customer-facing teams to turn ambiguous requirements into reliable systemsHelp establish strong engineering standards around testing, deployment, CI/CD, version control workflows, code review, and operational reliabilityMentor and collaborate with engineers across both software and AI disciplinesEvaluate emerging AI technologies pragmatically based on business impact, maintainability, and operational reliabilityRequired QualificationsUS Citizen or authorized to work in US5+ years of professional software engineering experience building production systemsStrong proficiency in PythonStrong backend engineering fundamentals and experience building scalable APIs, services, distributed systems, or workflow orchestration platformsProven hands-on experience building and shipping AI-powered applications using LLMs, generative AI APIs, agents, retrieval systems, or related technologies in production environmentsExperience designing and implementing agentic workflows, tool-calling systems, structured outputs, prompt pipelines, or retrieval-augmented generation architecturesStrong understanding of the practical challenges involved in production AI systems, including hallucination mitigation, evaluation, reliability, observability, latency, and cost managementExperience building production software systems with strong engineering standards around testing, QA, deployment, monitoring, and maintainabilityStrong understanding of modern software engineering practices, including Git workflows, code review, CI/CD, automated testing, operational debugging, and release managementExperience working with cloud infrastructure, preferably AWSExperience working with SQL and/or NoSQL databasesStrong debugging, systems-thinking, and problem-solving skillsAbility to operate effectively in fast-moving environments with evolving requirements and imperfect informationStrong communication skills and ability to collaborate across technical and non-technical teamsPreferred QualificationsExperience with Amazon Bedrock, AWS Lambda, Step Functions, S3, DynamoDB, RDS, SQS, EventBridge, or related AWS servicesExperience with LangGraph, LangChain, DSPy, Semantic Kernel, or similar orchestration frameworksExperience building multi-step agents that interact with tools, APIs, external systems, or business workflowsExperience implementing AI evaluation systems, prompt regression testing, trace analysis, or human-in-the-loop review workflowsExperience with vector databases and semantic retrieval systems such as OpenSearch, pgvector, Pinecone, Weaviate, FAISS, or similar technologiesExperience with observability and LLMOps tooling such as LangSmith, Arize, Helicone, Weights & Biases, OpenTelemetry, or similar platformsExperience balancing quality, latency, reliability, and cost tradeoffs in production AI systemsExperience mentoring engineers and helping establish strong engineering culture and development practicesExperience working in startup or high-ownership product environmentsAbility to think critically about edge cases, failure modes, operational risk, and long-term maintainabilityWhat Success Looks LikeAI systems that are reliable, observable, maintainable, and trusted by both customers and internal teamsEngineering practices that improve development velocity, operational quality, and long-term maintainabilityAI workflows that solve meaningful business problems rather than isolated demos or experimentsStrong collaboration between product engineering and applied AI effortsPragmatic adoption of AI technologies based on measurable business impact and operational reliability
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