Planera

Compensation

Full Description

About the Role Join Planera to build Manny, our AI scheduling assistant, and shape how construction schedulers work with AI on a modern Critical Path Method platform. You will 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. This is a hands-on applied AI role with a strong software engineering foundation and a focus on reliability, behavior quality, and user impact. You will work directly with the CTO and the lead AI engineer. Key Responsibilities 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 Preferred Experience with MongoDB and Redis Cloud experience (AWS or GCP), containers, and CI/CD Go experience, as most of our backend systems are written in Go, including the MCP tool server Practical experience with retrieval and augmentation (RAG), embeddings, and vector stores Familiarity with LangSmith or comparable LLM evaluation and tracing platforms Frontend or React familiarity for agent UI work Domain knowledge in construction tech, project management, or scheduling Tech Stack Python (Flask), Go, LangGraph/LangChain, LangSmith, MongoDB, Redis, S3, REST/websockets/SSE, Docker, AWS/GCP, Terraform, GitLab CI/CD Why Join Us Impact: Be at the forefront of transforming a $12.1 trillion industry. Build the AI that changes how the world plans and schedules construction. Culture: Join a smart, spirited team dedicated to innovation and excellence. Growth: Opportunity for professional growth and career advancement in a fast-paced start-up environment. Benefits Competitive salary, stock options, benefits package, and a dynamic work environment.