Staff AI Engineer
New
J
JobgetherAI software
Based in New ZealandFull-TimeStaff
Salary not disclosed
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Job Details
- Required Skills
- GraphQLPostgreSQLPythonDjangoRedisRESTful APIs
Requirements
- Extensive software engineering experience at Staff Engineer or equivalent level.
- Ability to lead complex, ambiguous initiatives as a hands-on individual contributor.
- Experience designing, building, and deploying production-grade LLM applications, AI agents, or generative AI features used by real customers.
- Ability to influence architectural decisions across teams, establish engineering standards, and drive initiatives from proposal through implementation.
- Experience building and maintaining AI evaluation frameworks using automated testing, agent traces, human feedback, and production monitoring.
- Strong understanding of AI agent architecture, tool design, and orchestration, including when deterministic logic is preferable to LLM-based solutions.
- Experience designing secure and reliable tool boundaries and human-in-the-loop workflows.
- Ability to balance performance, latency, security, reliability, and operational costs.
- Sound judgment in build-versus-buy decisions, model selection, and AI provider integrations.
- Backend and platform engineering expertise, particularly with Python, Django, PostgreSQL, Redis, asynchronous workers, and REST or GraphQL APIs.
- Ability to design scalable shared AI infrastructure with well-defined interfaces, efficient caching strategies, and robust tenant isolation.
- A measurement-driven approach that prioritizes evidence, evaluation, and reliability.
Responsibilities
- Lead the technical design, development, and delivery of AI assistants, contextual data systems, and generative AI features.
- Improve production AI systems, architecture, workflows, quality, reliability, and performance.
- Own major AI projects from architecture and planning through collaboration, deployment, and ongoing optimization.
- Influence AI architecture and shared platform infrastructure decisions across engineering teams.
- Establish AI quality frameworks with automated evaluations, regression testing, guardrails, agent tracing, human feedback, and production monitoring.
- Design and implement reliable AI agents and tool integrations, balancing deterministic code and LLM capabilities.
- Drive engineering initiatives such as AI evaluation standards, agentic software development practices, code review processes, and technical documentation.
- Collaborate with Product, Design, QA, Operations, and engineering stakeholders to translate business needs into technical solutions.
- Mentor engineers and promote knowledge sharing, architectural standards, and continuous improvement.
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