{"id":1293913,"url":"https://alion.io/job/givzey-applied-ai-engineer","title":"Applied AI Engineer","company":{"id":2077569,"name":"Givzey","domain":"givzey.com","url":"https://alion.io/company/givzey","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Breezy","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-01T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":135000,"max_usd":241000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":222},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Bedrock AgentCore","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"Git","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"Amazon EventBridge","optional":true},{"name":"Amazon S3","optional":true},{"name":"Arize Phoenix","optional":true},{"name":"AWS Lambda","optional":true},{"name":"AWS Step Functions","optional":true},{"name":"DSPy","optional":true},{"name":"DynamoDB","optional":true},{"name":"FAISS","optional":true},{"name":"Helicone","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LangSmith","optional":true},{"name":"LLMOps","optional":true},{"name":"OpenSearch","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"Weaviate","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-08-25T13:05:28Z","employer_posted_date":"2026-08-25","last_verified_at":"2026-10-01T23:40:53Z","board_verified":true,"closed_at":null,"days_open":37,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":37},"description":"Applied AI Engineer\nAbout Givzey & Version2.ai\nJoin the Future of Fundraising at Givzey!\nGivzey 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 $20M+ 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.\nAbout the Role\nWe’re hiring an Applied AI Engineer to build production AI systems that real customers depend on.\nThis 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.\nYou’ll work on everything from agent workflows and retrieval systems to backend APIs, evaluation tooling, observability, and production infrastructure.\nWe 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.\nWhat You’ll Work On\nAgentic AI workflows that automate complex business processes\nAI-powered product experiences that combine LLMs, retrieval, backend systems, and human review workflows\nRetrieval systems that connect AI agents to organization-specific knowledge and data\nBackend services and APIs that allow AI systems to safely interact with internal product workflows and data\nPrompting, evaluation, and observability systems that improve the quality and consistency of generated outputs\nMonitoring and debugging infrastructure for production AI systems\nHuman-in-the-loop review systems that combine automation with expert oversight\nInternal AI tooling, orchestration frameworks, and operational infrastructure\nResponsibilities\nDesign, build, and maintain production-grade AI systems and customer-facing AI features\nDevelop agentic workflows using LLMs, retrieval systems, tools, APIs, and backend services\nBuild backend services, orchestration systems, automation, and infrastructure supporting AI-powered workflows\nDesign and implement retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly\nIntegrate foundation models through platforms such as Amazon Bedrock or Agent Core\nDevelop robust prompting strategies, structured outputs, guardrails, and workflow logic for production use cases\nImplement evaluation systems for prompts, agents, and workflows, including regression testing, trace review, golden datasets, and human QA processes\nMonitor and improve production AI systems for quality, reliability, latency, observability, and cost efficiency\nDebug AI behavior through logs, traces, evaluations, user feedback, and production telemetry\nCollaborate closely with engineering, product, operations, and customer-facing teams to turn ambiguous requirements into reliable systems\nHelp establish strong engineering standards around testing, deployment, CI/CD, version control workflows, code review, and operational reliability\nMentor and collaborate with engineers across both software and AI disciplines\nEvaluate emerging AI technologies pragmatically based on business impact, maintainability, and operational reliability\nRequired Qualifications\nUS Citizen or authorized to work in US\n5+ years of professional software engineering experience building production systems\nStrong proficiency in Python\nStrong backend engineering fundamentals and experience building scalable APIs, services, distributed systems, or workflow orchestration platforms\nProven hands-on experience building and shipping AI-powered applications using LLMs, generative AI APIs, agents, retrieval systems, or related technologies in production environments\nExperience designing and implementing agentic workflows, tool-calling systems, structured outputs, prompt pipelines, or retrieval-augmented generation architectures\nStrong understanding of the practical challenges involved in production AI systems, including hallucination mitigation, evaluation, reliability, observability, latency, and cost management\nExperience building production software systems with strong engineering standards around testing, QA, deployment, monitoring, and maintainability\nStrong understanding of modern software engineering practices, including Git workflows, code review, CI/CD, automated testing, operational debugging, and release management\nExperience working with cloud infrastructure, preferably AWS\nExperience working with SQL and/or NoSQL databases\nStrong debugging, systems-thinking, and problem-solving skills\nAbility to operate effectively in fast-moving environments with evolving requirements and imperfect information\nStrong communication skills and ability to collaborate across technical and non-technical teams\nPreferred Qualifications\nExperience with Amazon Bedrock, AWS Lambda, Step Functions, S3, DynamoDB, RDS, SQS, EventBridge, or related AWS services\nExperience with LangGraph, LangChain, DSPy, Semantic Kernel, or similar orchestration frameworks\nExperience building multi-step agents that interact with tools, APIs, external systems, or business workflows\nExperience implementing AI evaluation systems, prompt regression testing, trace analysis, or human-in-the-loop review workflows\nExperience with vector databases and semantic retrieval systems such as OpenSearch, pgvector, Pinecone, Weaviate, FAISS, or similar technologies\nExperience with observability and LLMOps tooling such as LangSmith, Arize, Helicone, Weights & Biases, OpenTelemetry, or similar platforms\nExperience balancing quality, latency, reliability, and cost tradeoffs in production AI systems\nExperience mentoring engineers and helping establish strong engineering culture and development practices\nExperience working in startup or high-ownership product environments\nAbility to think critically about edge cases, failure modes, operational risk, and long-term maintainability\nWhat Success Looks Like\nAI systems that are reliable, observable, maintainable, and trusted by both customers and internal teams\nEngineering practices that improve development velocity, operational quality, and long-term maintainability\nAI workflows that solve meaningful business problems rather than isolated demos or experiments\nStrong collaboration between product engineering and applied AI efforts\nPragmatic adoption of AI technologies based on measurable business impact and operational reliability","description_format":"text","description_chars":7349,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Professional Services","Nonprofits & Foundations"],"lifecycle":[{"event":"open","at":"2026-09-26T08:44:53Z"}],"liveness":{"score":7,"band":"cold","label":"Long shot","p_open":1,"p_active":0.209,"p_room":0.35,"age_days":36,"expected_fill_days":7,"reasons":["conf:11","win:tail"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/givzey-applied-ai-engineer","json_url":"https://alion.io/job/givzey-applied-ai-engineer.json","meta":{"generated_at":"2026-10-02T03:06:45Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4297,"day_limit":5000,"remaining_today":703,"minute_limit":60,"resets_at":"2026-10-03T00:00:00Z"}}}