Software Developer building AI agents for Guideline's advertising solutions, ensuring accuracy and reliability in workflows. Collaborating with cross-functional teams to enhance media performance.
Responsibilities
Design and ship multi-step AI agents using modern orchestration frameworks (Claude, OpenAI Agents SDK, or equivalent), including prompt design, state management, tool calling, and human-in-the-loop control.
Build and maintain MCP servers and tool integrations connecting agents to internal services, data warehouses, and third-party APIs; define clean schemas, error handling, and least-privilege authorization scopes.
Implement retrieval-augmented generation (RAG) pipelines — ingestion, chunking, embedding, hybrid retrieval, reranking — grounded in Guideline’s proprietary spend, pricing, and media datasets.
Develop offline and online evaluations (LLM-as-judge, deterministic checks, golden sets, regression suites) that measure agent quality, tool-use correctness, task completion, latency, and cost before each release.
Instrument agents with end-to-end tracing and observability (e.g., OpenTelemetry, LangSmith, MLflow) and operate them in production: monitor drift, regressions, prompt-injection attempts, and hallucination rates.
Apply security and safety controls — input/output filtering, prompt-injection defenses, sandboxed tool execution, PII handling, data residency — in collaboration with Security and Compliance.
Optimize for cost and latency through model routing, caching, batching, and choosing the right level of agency — deterministic workflow vs. autonomous agent — for each problem.
Write production-quality Python with strong testing discipline; contribute to backend services, APIs, and CI/CD pipelines that host agent workloads.
Partner with product, data science, and design to translate ambiguous business problems into well-scoped agent specifications, success metrics, and rollout plans.
Stay current on the rapidly evolving agent ecosystem and bring back patterns the team should adopt — or reject — with a clear rationale.
Requirements
3+ years of professional software engineering experience shipping production systems, with at least 1 year focused on LLM-powered or agentic applications.
Strong Python skills, including async programming, type hints, testing, and clean API design. Comfort with Git-based development and modern CI/CD.
Hands-on experience with one or more agent frameworks (LangGraph, LangChain, OpenAI Agents SDK, Anthropic SDK, CrewAI, AutoGen, Pydantic AI) and provider APIs from at least one of OpenAI, Anthropic, or Google.
Practical experience with the Model Context Protocol (MCP) or equivalent tool-protocol patterns; ability to design clean tool interfaces and reason about authorization scopes.
Demonstrated experience building RAG systems, including vector stores (e.g., pgvector, Pinecone, Weaviate), embedding selection, hybrid search, and reranking.
Working knowledge of agent evaluation: designing evals, building golden sets, running LLM-as-judge, and interpreting results to make ship/no-ship decisions.
Familiarity with prompt engineering tradecraft and an empirical mindset — preferring measurement over intuition for agent behavior.
Solid grasp of cloud infrastructure (AWS, GCP, or Azure), containers (Docker), and at least one production runtime — Kubernetes, serverless, or comparable.
Understanding of LLM security and safety: prompt injection, data exfiltration, output validation, sandboxing, and least-privilege tool access.
Strong written and verbal communication; ability to write design docs, present trade-offs, and collaborate across product, data, and security functions.
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