2 months ago
Remote, United StatesSenior
Responsibilities
- Design, build, and maintain production-grade AI systems and customer-facing AI features.
- Develop agentic workflows using LLMs, retrieval systems, tools, APIs, and backend services.
- Build backend services, orchestration systems, automation, and infrastructure for AI-powered workflows.
- Design and implement RAG systems with ingestion pipelines, embeddings, semantic retrieval, and context assembly.
- Integrate foundation models through platforms such as Amazon Bedrock and Agent Core.
- Develop prompting strategies, structured outputs, guardrails, and workflow logic for production use cases.
- Implement evaluation systems for prompts, agents, and workflows, including regression testing, trace review, golden datasets, and human QA.
- Monitor, debug, and improve production AI systems using logs, traces, evaluations, user feedback, and telemetry.
- Collaborate with engineering, product, operations, and customer-facing teams to turn ambiguous requirements into reliable systems.
- Help establish standards for testing, deployment, CI/CD, version control, code review, and operational reliability.
- Mentor and collaborate with engineers across software and AI disciplines.
- Evaluate emerging AI technologies based on business impact, maintainability, and operational reliability.
Requirements
- 5+ years of professional software engineering experience building production systems.
- Strong proficiency in Python and strong backend engineering fundamentals.
- Experience building scalable APIs, services, distributed systems, or workflow orchestration platforms.
- Hands-on experience building and shipping production AI applications using LLMs, generative AI APIs, agents, retrieval systems, or related technologies.
- Experience designing agentic workflows, tool-calling systems, structured outputs, prompt pipelines, or RAG architectures.
- Understanding of production AI challenges including hallucination mitigation, evaluation, reliability, observability, latency, and cost management.
- Experience building production software with strong standards for testing, QA, deployment, monitoring, and maintainability.
- Understanding of Git workflows, code review, CI/CD, automated testing, operational debugging, and release management.
- Experience with cloud infrastructure, preferably AWS, and with SQL and/or NoSQL databases.
- Strong debugging, systems-thinking, problem-solving, communication, and cross-functional collaboration skills.
- US citizenship or authorization to work in the US is required.
- Preferred: experience with AWS services, AI orchestration frameworks, multi-step agents, evaluation systems, vector databases, semantic retrieval, observability or LLMOps tooling, and human-in-the-loop workflows.
- Preferred: experience balancing quality, latency, reliability, and cost tradeoffs in production AI systems; mentoring engineers; and working in startup or high-ownership product environments.
- Preferred: ability to assess edge cases, failure modes, operational risk, and long-term maintainability.
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.