Staff Engineer, Karlo Platform

Kargo•New York, NY

34d•$200,000 - $220,000

About The Position

Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our dynamic teams work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do to deliver unique ad experiences across the world’s most premium platforms. Taking a Creative Science approach to all we do, we continuously innovate solutions that outperform industry benchmarks and client expectations. Kargo is growing rapidly and currently has offices in NYC, Chicago, LA, Dallas, Sydney, Auckland, London and Waterford, Ireland. 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.

Requirements

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

Nice To Haves

AdTech or programmatic experience a plus

Responsibilities

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