Senior Applied AI Engineer

Job not on LinkedIn

🔥 5 minutes ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🤖 AI Engineer

👻 Ghost score 12%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Givzey

WebsiteLinkedInAll Job Openings

1 - 10 employees

💸 Finance

🤝 Non-profit

☁️ SaaS

Finance • Non-profit • SaaS

Givzey is a platform that offers intelligent solutions for the fundraising process, including smart gift agreements, dynamic workflows, automated pledge reminders, and intelligent invoicing. The company aims to streamline and scale multi-year giving and one-time gifts, enhancing the donor experience while providing tools for fundraising teams to improve efficiency. Givzey is utilized by educational institutions and other organizations to secure and manage donations effectively.

📋 Description

• Lead small engineering teams in designing, developing, and delivering complex, high-impact AI initiatives • Own technical execution from initial design through implementation, testing, deployment, and ongoing operation • Translate ambiguous product requirements into technical approaches, engineering tasks, and delivery milestones • Drive architectural discussions, evaluate tradeoffs, and establish implementation strategies • Coordinate engineering efforts, identify dependencies and blockers, and maintain accountability for delivery • Contribute production code and solve challenging technical problems alongside the team • Collaborate with Product, Engineering leadership, and stakeholders to deliver meaningful business outcomes • Design, build, and maintain production-grade AI systems using LLMs, agentic workflows, retrieval architectures, and backend services • Develop agent workflows with tool calling, structured outputs, multi-step reasoning, deterministic validation, and human-in-the-loop controls • Build and optimize RAG systems, including ingestion pipelines, embeddings, semantic retrieval, and context assembly • Integrate foundation models through Amazon Bedrock and AWS AgentCore • Develop AI evaluation frameworks including regression testing, golden datasets, trace analysis, and quality measurement • Improve AI systems for reliability, accuracy, latency, observability, and cost efficiency • Design systems to handle unpredictable model behavior, edge cases, operational failures, and production constraints • Elevate engineering standards through code reviews, architectural guidance, mentorship, and technical leadership • Mentor engineers in software design, debugging, testing, system architecture, and production engineering practices • Establish best practices for code quality, automated testing, documentation, maintainability, and operational readiness • Improve engineers' technical judgment, ownership, independence, and problem-solving capabilities

🎯 Requirements

• US citizen or authorized to work in the United States • 7+ years of professional software engineering experience • Strong proficiency in Python and modern backend software engineering • Experience designing and delivering production systems • Technical leadership experience for engineering projects or small teams, including architecture, implementation, mentorship, and delivery • Hands-on experience designing, building, and shipping production AI applications using LLMs, agentic workflows, retrieval systems, or related technologies • Strong knowledge of backend architecture, APIs, distributed systems, databases, and workflow orchestration • Experience designing agentic workflows, tool-calling systems, structured outputs, or retrieval-augmented generation architectures • Understanding of production AI challenges, including model reliability, hallucination mitigation, evaluation, observability, latency, and cost management • Experience with automated testing, code review, CI/CD, system design, and maintainability • Experience with cloud infrastructure, preferably AWS • Ability to mentor engineers, provide constructive technical feedback, and influence engineering practices • Strong technical judgment and debugging skills • Ability to navigate complex systems and ambiguous requirements • Ability to balance hands-on engineering contributions with technical leadership responsibilities • Excellent communication and collaboration skills across engineering, product, and business teams • Experience operating in fast-moving environments where ownership, adaptability, and execution are essential • Preferred: Experience with Amazon Bedrock, AWS AgentCore, Lambda, Step Functions, S3, DynamoDB, RDS, SQS, or related AWS services • Preferred: Experience with LangGraph, LangChain, DSPy, Semantic Kernel, or similar agent orchestration frameworks • Preferred: Experience building multi-step autonomous agents interacting with tools, APIs, external systems, or business workflows • Preferred: Experience developing AI evaluation frameworks, prompt regression testing, trace analysis, or human-in-the-loop review systems • Preferred: Experience with vector databases and semantic retrieval technologies such as OpenSearch, pgvector, Pinecone, Weaviate, or FAISS • Preferred: Experience with LLM observability and evaluation platforms such as LangSmith, Arize, Helicone, Weights & Biases, or OpenTelemetry • Preferred: Experience leading technical initiatives within startup or rapidly scaling SaaS organizations • Preferred: Experience improving engineering practices and increasing technical maturity

🏖️ Benefits

• Fully remote work arrangement • Opportunity to work on high-impact AI initiatives • Technical leadership and mentorship opportunities • Participation in building sophisticated AI capabilities and production-grade systems

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