4 hours ago
Remote, United StatesSenior
Responsibilities
- Lead small engineering teams in designing, developing, and delivering complex AI initiatives.
- Own technical execution from initial design through implementation, testing, deployment, and ongoing operation.
- Build production AI systems using LLMs, agentic workflows, retrieval architectures, and backend services.
- Develop tool-calling workflows, structured outputs, multi-step reasoning, deterministic validation, and human-in-the-loop controls.
- Build retrieval-augmented generation systems with ingestion pipelines, embeddings, semantic retrieval, and context assembly.
- Integrate foundation models through Amazon Bedrock and AWS AgentCore.
- Develop evaluation frameworks using regression testing, golden datasets, trace analysis, and quality measurement.
- Improve AI system reliability, accuracy, latency, observability, and cost efficiency.
- Provide architectural guidance, code reviews, mentorship, and technical leadership to improve engineering standards.
- Collaborate with Product, Engineering leadership, and business stakeholders to deliver meaningful outcomes.
Requirements
- 7+ years of professional software engineering experience designing and delivering production systems.
- Strong proficiency in Python and modern backend software engineering.
- Experience providing technical leadership for engineering projects or small teams, including architecture, implementation, mentorship, and delivery.
- Hands-on experience 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.
- Commitment to software engineering practices including automated testing, code review, system design, and maintainability.
- Experience with cloud infrastructure, preferably AWS.
- Ability to mentor engineers, provide technical feedback, navigate ambiguity, and balance hands-on engineering with technical leadership.
- US citizenship or authorization to work in the United States.
- Preferred experience with Amazon Bedrock, AWS AgentCore, Lambda, Step Functions, S3, DynamoDB, RDS, SQS, LangGraph, LangChain, DSPy, Semantic Kernel, vector databases, semantic retrieval, and AI observability or evaluation platforms.
- Preferred experience leading technical initiatives in startup or rapidly scaling SaaS organizations and improving engineering practices.
Benefits
- Fully remote work arrangement.
- Opportunity to lead high-impact AI initiatives and improve the technical maturity of the engineering organization.
- Hands-on mentorship, architectural leadership, and collaboration across engineering, product, and business teams.
Categories
About Givzey
Givzey builds fundraising software for nonprofits, schools, and universities. Its SaaS platform includes a standalone Gift Agreement product that lets fundraisers generate and secure gift commitments in seconds, plus tools that turn donor data into automated outreach and pledges. Headquartered in Boston and privately held, the company serves higher-education and nonprofit development teams.