Staff Engineer, Karlo Platform

The Opportunity

Karlo is Kargo's agentic ad-buying platform, where customers use conversational AI agents to build campaigns, creatives, targeting strategies, and analytics. As an AI Platform Engineer on the Karlo Platform team, you'll own the shared infrastructure every agent runs on — observability, model evaluation, the LangGraph runtime, checkpointing and context memory management, shared services, and the developer experience tying it all together. Your goal is to make the platform invisible to the agent teams building on it: so reliable, scalable, and well-documented that they can focus entirely on their agents.

The Daily To-Do

• Own and harden the core LangGraph-based agent hosting infrastructure so every agent team can build and deploy with confidence — no undocumented behaviors, no surprise failures
• Design and deliver a shared evaluation framework that lets agent teams validate correctness, measure quality, and catch regressions before they reach customers
• Ship production-ready shared services — authentication, billing, and integration layers including Salesforce and CXP — that agent teams consume cleanly instead of rebuilding
• Own checkpointing and agent context memory — state persistence, history compaction, and retrieval of relevant prior context across long-running, multi-turn conversations
• Instrument the platform end-to-end: every agent, every session, every failure, with tracing across LLM calls plus latency, cost, and failure-mode visibility
• Build the SDKs, documentation, and onboarding tooling that cut a new agent team's ramp time from weeks to days
• Partner closely with agent teams to translate their needs into platform capabilities, and measurably reduce the infrastructure overhead they carry today

Qualifications

• 7–10 years of software engineering experience, including at least 2 years building production AI or agentic systems — shipped and operated, not prototyped
• Deep hands-on experience with LangGraph in production: graph construction, node and edge design, conditional routing, state management, and agent lifecycle management; plus the broader LangChain ecosystem and LangSmith for tracing, evaluation, and dataset management
• Command of agentic design patterns — ReAct, Plan-and-Execute, multi-agent orchestration, human-in-the-loop workflows, and tool use — with experience operating multi-agent systems and state handoff across agent boundaries
• Strong LLM foundations: prompt engineering, context window management, memory architectures for multi-turn agents, token and cost optimization, RAG architectures (vector stores, embedding pipelines, retrieval, reranking), and model selection tradeoffs
• Experience with LLM evaluation methodologies and production observability for AI systems, including the ability to debug non-deterministic agent behavior methodically
• Strong backend fundamentals across distributed systems, API design, and event-driven architecture; TypeScript required and Golang a strong plus; cloud infrastructure (AWS, GCP, or Azure), containerization, and CI/CD
• Experience building shared platform infrastructure consumed by multiple teams, with a track record of improving developer experience through SDKs, documentation, or internal tooling; B.S. or higher in Computer Science, Engineering, Mathematics, or equivalent practical experience; AdTech or programmatic experience a plus