Senior AI Agent Engineer

Job 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.