Senior AI Agent Engineer

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🔥 13 hours ago

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Planera Inc

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

☁️ SaaS

🤝 B2B

🏢 Enterprise

🔥 Funding within the last year

💰 $8M Series A - Planera on 2025-10

SaaS • B2B • Enterprise

Planera Inc is a SaaS company that provides a modern, visual CPM scheduling platform for construction teams, with special focus on data center builds and complex construction projects. Its digital whiteboard (Canvas), field-facing iPad app, DCMA quality checks, and integrated field-to-master schedule workflows enable real-time collaboration between office and field, risk simulation, resource planning, and portfolio-level visibility. Planera integrates with tools like Procore, Autodesk, Primavera P6, and Microsoft Project and is positioned as an easy-to-use replacement for legacy scheduling software for builders, preconstruction teams, and enterprise project organizations.

📋 Description

• 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