Senior AI Agent Engineer
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Job Description
Join Planera to build Manny, an AI scheduling assistant for construction. Seeking a Senior AI Agent Engineer with strong software engineering background in LLM features.
Responsibilities:
- Join Planera to build Manny, our AI scheduling assistant, and shape how construction schedulers work with AI on a modern Critical Path Method platform.
- Own agent features end to end: designing and evolving the LangGraph/LangChain agent, engineering prompts and tools, integrating LLMs across providers, and holding response quality to a high bar with a real evaluation and observability stack.
- Design, build, and own Manny features end to end across the agent backend, tools, and UI
- Improve agent behavior, reliability, and answer quality through prompt engineering, tool design, and changes to the agent control flow
- Evolve the agent architecture: ReAct loop, routing and controller logic, multi-node graphs, tool selection, and streaming responses
- Integrate and tune LLMs across providers (Anthropic, OpenAI, Google), balancing quality, latency, and cost, including prompt caching and model selection
- Design and extend Manny's tool surface through the MCP server that connects the agent to Planera's scheduling services
- Build and own the evaluation loop: golden datasets, automated evaluators, snapshot-based replay, and offline and online quality metrics
- Implement observability for agent runs with tracing, metrics, and structured logging, and use it to debug and improve behavior in production
- Ensure safe, sandboxed execution of model-generated code and safe handling of tool side effects and mutations
- Collaborate with product, backend, and frontend to deliver AI features end to end
Requirements:
- 4+ years of software engineering experience, including recent hands-on work building production LLM features.
- Strong proficiency in Python building production services
- Hands-on experience building agentic systems with LLMs: tool and function calling, ReAct or similar loops, and orchestration frameworks such as LangChain/LangGraph
- Practical prompt engineering skill: shaping model behavior reliably, debugging failures from traces, and managing large prompts and token cost
- Experience evaluating LLM systems: building datasets, writing evaluators, catching regressions, and using tracing and observability tooling
- Experience with the Model Context Protocol (MCP) or building tool and function-calling integrations for LLMs
- Solid understanding of API design (REST, websockets, SSE and streaming) and interservice communication
- Product mindset with a focus on user impact and pragmatic tradeoffs
- Excellent remote communication skills.
Benefits:
- Competitive salary
- Stock options
- Benefits package
- Dynamic work environment
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