Pushpay
From Pushpay's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
The Role in 60 Seconds Most companies are still experimenting with AI wrappers in local environments.
Most companies are still experimenting with AI wrappers in local environments. At Pushpay, we are building production-grade agentic systems that directly amplify how our entire engineering organization builds, ships, and operates software.
As a Staff AI Platform Engineer, you will operate with significant autonomy to architect, run, and scale our internal agentic platforms and developer tooling (like PR review agents, SDLC copilots, and context engines). You won't just build these systems—you will own their operational discipline, cost governance, evaluation suites, and reliability in production, acting as a force multiplier for every engineer across Pushpay.
True Engineering Leverage: You aren't building internal gimmicks. You are designing systems that fundamentally transform our SDLC and reduce friction for every product team.
Production-Grade Non-Determinism: You treat agents as distributed, operational workloads—implementing token budgets, trace-level observability (OTEL, Langfuse), SLOs/SLIs, and eval-driven regression testing before code hits main.
Influence Without Dictatorship: You won't enforce top-down mandates. You will drive org-wide adoption of tools like Claude Code and custom MCP implementations through technical credibility, pairing with teams, and building platforms engineers want to use.
Architect & Build Agentic Systems: Design internal supervisor loops, router patterns, and state machines using the Claude Agent SDK, AWS Bedrock, and frameworks like LangGraph.
Run Workloads at Scale: Establish SLOs for agent success rates, latency, and token efficiency. Take point on observability, prompt versioning, and incident response when agents misbehave or hallucinate.
Elevate Developer Experience: Identify SDLC bottlenecks, optimize build/CI performance, refine local dev setups (LocalStack, .NET Aspire), and tune AWS serverless architectures (Lambda cold-starts, SnapStart).
Set the Bar for AI Safety & Quality: Define reusable component models, context retrieval (RAG) architecture, and guardrails to ensure autonomous systems degrade safely.
8+ Years in Software Engineering with significant time spent as a technical lead in Platform, Infrastructure, or DevEx.
Production Agentic Experience: You have actually operated non-deterministic LLM/agentic workloads in production—managing cost governance, tracing, and eval pipelines, not just building prototype wrapper apps.
Backend & Cloud Mastery: Deep proficiency in C# or Python paired with strong AWS cloud-native expertise (Lambda, API Gateway, Bedrock, IAM/KMS).
Developer Empathy & Leadership: A track record of driving tool adoption and technical standards across multiple teams through pairing, mentorship, and clear communication rather than authority.
Tech Stack You'll Touch
Claude Agent SDK • AWS Bedrock • LangGraph / LangChain • OTEL • Langfuse • New Relic • C# / .NET • Python • AWS Lambda • .NET Aspire • LocalStack
Ready to build the platform that powers the future of our engineering organization?
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