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.