Senior AI Agent Engineer

Planera Inc

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.

Posted 6/30/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and integrating LLM features, with a strong focus on prompt engineering, API design, and observability. Capable of collaborating across teams to deliver impactful AI solutions in production environments.

Highest-signal resume keywords

Python ProficiencyLLM Feature DevelopmentPrompt EngineeringAPI DesignObservability Tooling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringProduction LLM FeaturesAgentic SystemsTool and Function CallingReAct LoopsLangChainLangGraphDataset EvaluationInterservice CommunicationModel Context Protocol

Soft Skills

Remote CommunicationProduct Mindset

Tools & Technologies

LLMsMCP ServerRESTWebsocketsSSEStreaming

Industry Keywords

Critical Path MethodAI Scheduling AssistantEvaluation LoopQuality MetricsDebugging

Tech Stack

Tools & technologies

Node.jsPythonReact

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

Competitive salary
Stock options
Benefits package
Dynamic work environment