{"id":3477101,"url":"https://alion.io/job/givzey-senior-applied-ai-engineer","title":"Senior 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":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-02T06:13:15Z"}},"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":134000,"max_usd":255000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":370},"experience_years_min":7,"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":"Function Calling","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false},{"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":"LLM","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":"2026-10-29T15:13:38Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-02T07:11:25Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About Givzey | Version2.ai\nGivzey | Version2.ai is a fully remote, AI-native company that created the Autonomous Fundraising category in 2024, when it researched, developed, and deployed the world’s first Autonomous AI to address the labor shortage and capacity gap in nonprofit fundraising. Fundraising teams have a finite capacity to reach donors with one-to-one communication, on average leaving 97% of donors to their organization receiving only marketing communications. A labor shortage compounds the problem: even when organizations want to hire more fundraisers, they often can't find them. The result is a significant capacity gap, where teams can't reach large pools of high-potential donors, resulting in important organizations with world-changing missions being unable to fully execute on their visions. \nVersion2.ai's Virtual Engagement Officers (VEOs) close the labor shortage gap with each VEO independently managing a portfolio of up to 1,000 donors, delivering personalized outreach through text, email, video, and robotically handwritten notes year-round. VEOs don't just make staff more efficient; they manage donor portfolios independently, freeing human fundraisers to focus their time on the highest-value relationships while also closing transformational revenue for these organizations. \nVEOs have already generated over $20M in fundraising revenue to date, including individual gifts as large as $100,000, at an opt-out rate of just 0.07%. Organizations deploying a VEO see 4x annual ROI on average, and the top 20% see 8x or higher. More than 250 VEOs are now deployed across higher education, healthcare, and nonprofit organizations in the U.S. and Canada, including UNICEF USA, Bucknell University, Wake Forest University, and William & Mary.\nAdam Martel founded Givzey four years ago and leads the company as CEO. He previously founded, scaled, and sold Gravyty, the first company to develop and sell AI-first solutions to nonprofit organizations. \nAbout the Role\nWe're hiring a Senior Applied AI Engineer to lead the technical design and delivery of high-impact AI initiatives across our products.\nThis is a senior individual contributor and technical leadership role for an experienced software engineer with deep expertise in building production AI systems. You'll lead small teams of engineers responsible for delivering sophisticated AI capabilities, from agentic workflows and retrieval systems to autonomous business processes and intelligent product experiences.\nWe're looking for someone who brings more than technical expertise. You should be capable of taking ownership of complex initiatives, breaking ambiguous problems into actionable engineering work, making sound architectural decisions, and guiding a team from concept through production delivery.\nEqually important, you'll play a significant role in raising the technical maturity of our engineering organization. Through mentorship, code reviews, architectural guidance, and hands-on collaboration, you'll help establish higher standards for software quality, testing, reliability, and maintainability.\nWe value engineers who combine deep technical ability with practical judgment, strong ownership, and a genuine investment in making the people around them better.\nWhat You'll Work On\nAgentic AI systems that autonomously execute complex, multi-step business workflows.\nAI-powered product experiences combining LLMs, retrieval, backend services, and human oversight.\nRetrieval and knowledge systems that connect AI agents to organization-specific information.\nBackend services, APIs, and tool integrations enabling AI systems to safely interact with production applications.\nEvaluation, observability, and testing frameworks that improve the quality, consistency, and reliability of AI behavior.\nOrchestration systems and infrastructure supporting scalable, production-grade AI applications.\nEngineering tooling and practices that improve development velocity, software quality, and operational reliability.\nResponsibilities\nTechnical Leadership & Delivery\nLead small teams of engineers in the design, development, and delivery of complex, high-impact AI initiatives.\nOwn the technical execution of projects from initial design through implementation, testing, deployment, and ongoing operation.\nTranslate ambiguous product requirements into clear technical approaches, actionable engineering tasks, and achievable delivery milestones.\nDrive architectural discussions, evaluate technical tradeoffs, and establish sound implementation strategies.\nCoordinate engineering efforts, identify dependencies and blockers, and maintain accountability for technical execution and delivery commitments.\nRemain actively involved in implementation, contributing production code and solving challenging technical problems alongside the team.\nCollaborate closely with Product, Engineering leadership, and stakeholders to ensure technical solutions deliver meaningful business outcomes.\nApplied AI Engineering\nDesign, build, and maintain production-grade AI systems using LLMs, agentic workflows, retrieval architectures, and backend services.\nDevelop sophisticated agent workflows involving tool calling, structured outputs, multi-step reasoning, deterministic validation, and human-in-the-loop controls.\nBuild and optimize retrieval-augmented generation (RAG) systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly.\nIntegrate foundation models through platforms such as Amazon Bedrock and AWS AgentCore.\nDevelop evaluation frameworks for AI workflows, including regression testing, golden datasets, trace analysis, and quality measurement.\nImprove production AI systems for reliability, accuracy, latency, observability, and cost efficiency.\nDesign systems that safely handle unpredictable model behavior, edge cases, operational failures, and real-world production constraints.\nEngineering Excellence & Mentorship\nElevate engineering standards through thoughtful code reviews, architectural guidance, hands-on mentorship, and technical leadership.\nMentor engineers in software design, debugging, testing strategies, system architecture, and production engineering practices.\nEstablish and reinforce best practices around code quality, automated testing, documentation, maintainability, and operational readiness.\nHelp engineers develop stronger technical judgment, ownership, independence, and problem-solving capabilities.\nIdentify recurring technical weaknesses, unnecessary complexity, and process inefficiencies, and actively work to address them.\nPromote a culture of thoughtful engineering where solutions are not simply functional, but reliable, understandable, testable, and maintainable.\nLead by example through high-quality implementation, effective collaboration, constructive feedback, and strong personal accountability.\nRequired Qualifications\nUS citizen or authorized to work in the United States.\n7+ years of professional software engineering experience, with substantial experience designing and delivering production systems.\nStrong proficiency in Python and modern backend software engineering.\nDemonstrated experience providing technical leadership for engineering projects or small teams, including architecture, implementation, mentorship, and delivery.\nProven hands-on experience designing, building, and shipping production AI applications using LLMs, agentic workflows, retrieval systems, or related technologies.\nStrong knowledge of backend architecture, APIs, distributed systems, databases, and workflow orchestration.\nExperience designing agentic workflows, tool-calling systems, structured outputs, or retrieval-augmented generation architectures.\nStrong understanding of production AI challenges, including model reliability, hallucination mitigation, evaluation, observability, latency, and cost management.\nDemonstrated commitment to software engineering best practices, including automated testing, code review, CI/CD, system design, and maintainability.\nExperience working with cloud infrastructure, preferably AWS.\nProven ability to mentor engineers, provide constructive technical feedback, and influence engineering practices.\nStrong technical judgment, debugging skills, and the ability to navigate complex systems and ambiguous requirements.\nAbility to balance hands-on engineering contributions with technical leadership responsibilities.\nExcellent communication and collaboration skills, particularly when working across engineering, product, and business teams.\nExperience operating in fast-moving environments where ownership, adaptability, and execution are essential.\nPreferred Qualifications\nExperience with Amazon Bedrock, AWS AgentCore, Lambda, Step Functions, S3, DynamoDB, RDS, SQS, or related AWS services.\nExperience with LangGraph, LangChain, DSPy, Semantic Kernel, or similar agent orchestration frameworks.\nExperience building multi-step autonomous agents that interact with tools, APIs, external systems, or business workflows.\nExperience developing AI evaluation frameworks, prompt regression testing, trace analysis, or human-in-the-loop review systems.\nExperience with vector databases and semantic retrieval technologies such as OpenSearch, pgvector, Pinecone, Weaviate, or FAISS.\nExperience with LLM observability and evaluation platforms such as LangSmith, Arize, Helicone, Weights & Biases, or OpenTelemetry.\nExperience leading technical initiatives within startup or rapidly scaling SaaS organizations.\nExperience improving engineering practices, introducing development standards, or helping teams increase their technical maturity.\nExperience balancing technical debt, delivery speed, operational reliability, and long-term architectural decisions.\nWho Thrives in This Role\nWe're looking for an engineer who naturally takes ownership and leads through technical expertise, sound judgment, and example.\nSomeone who can look at a complicated technical problem, develop a clear approach, bring other engineers along, and drive the work through completion.\nSomeone who cares deeply about code quality and engineering craftsmanship, but understands the importance of pragmatism and delivering real value.\nSomeone who enjoys mentoring engineers, challenging assumptions, providing constructive feedback, and helping teammates become more capable and independent.\nSomeone who understands that technical leadership means being accountable not only for their own contributions, but also for the quality and success of the work they help lead.\nAbove all, we're looking for someone who makes both our AI systems and our engineering organization stronger.\nWhat Success Looks Like\nHigh-impact AI initiatives are delivered successfully through effective technical leadership, clear ownership, and strong collaboration.\nAI systems are reliable, observable, maintainable, and trusted by customers and internal teams.\nEngineers working alongside you demonstrate measurable growth in technical ability, independence, and engineering judgment.\nCode quality, testing practices, architectural consistency, and operational reliability improve across the teams you lead.\nComplex technical initiatives move from ambiguous requirements to production-ready solutions with greater consistency and predictability.\nEngineering decisions balance innovation, delivery speed, maintainability, and long-term business value.\nYour technical leadership enables the broader organization to deliver increasingly sophisticated AI capabilities with 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