Who We Are
Inorsa is an AI-first automation platform transforming how mission-critical infrastructure is designed, maintained, and upgraded. We serve telecom, fiber, data centers, and more, automating everything from document ingestion to drawing generation. Our platform helps infrastructure owners build faster, safer, and more cost-effectively.

We're a fast-growing, highly technical team solving complex engineering problems. Every member of our team has a direct impact on product quality, customer outcomes, and how infrastructure is delivered now and into the future.

Why Now

As our agentic platform matures and enterprise deployments deepen, we need a technical leader who can close the gap between AI capability and production-grade reliability. We aren't competing on general intelligence—we're winning by being the deepest AI platform in the engineering vertical. This role is the tip of that spear.

What You'll Do

Architect Agentic Workflows

Design and implement multi-actor state machines using LangGraph, including loops, error-recovery paths, and human-in-the-loop checkpoints
Own the observability and evaluation strategy using LangSmith: trace agentic paths, version prompts, and run regression tests against gold-standard engineering datasets
Build custom evaluators (LLM-as-judge) and use traces to identify exactly where multi-step agentic chains fail in production

Drive Vertical Domain Integration

Collaborate directly with structural and telecom domain experts to encode specialized engineering knowledge into agentic tools
Translate 30+ years of practitioner tribal knowledge into reliable, production-grade automation
Work cross-functionally with engineers, product managers, and field teams to ensure AI outputs meet the accuracy standards required in high-stakes engineering environments

Set the Technical Standard

Lead by example on AI-native development: Cursor, Claude Code, GitHub Copilot, and equivalent tooling
Automate the grunt work of coding to concentrate team energy on architecture and domain logic
Review code, mentor engineers, and raise the bar on quality, performance, and maintainability across the AI stack
Communicate progress, risks, and architectural tradeoffs clearly with engineering leadership and product

What You Bring
Experience

6+ years of software engineering experience, including meaningful time building and shipping AI/ML systems in production
Expert-level proficiency in LangGraph: persistent state management, checkpointers, complex graph topologies
Deep experience with LangSmith or equivalent observability tooling for agentic systems
Strong async Python and experience deploying scalable AI services in cloud environments (AWS or GCP)
Experience working with unstructured or domain-specific data: technical specs, site reports, GIS data, or similar
Familiarity with the broader stack: TypeScript/Node.js, SQL, cloud infrastructure

Mindset

Anti-hype filter: you'd rather ship a simple, working solution that helps an engineer today than an experimental feature that breaks tomorrow
Vertical depth orientation: you understand that edge comes from domain mastery, not breadth of capability
Intellectually curious and humble: excited to learn why a structural detail fails from a construction veteran, then figure out how to teach an agent to catch it
High ownership: cares about whether the thing works in the field, not just whether it passes a demo

Bonus

Prior experience in telecom infrastructure, structural engineering workflows, or adjacent technical verticals
Track record building evaluation frameworks and prompt regression pipelines at scale
Experience supporting a company through Series A or early hypergrowth phases

Key Benefits

Medical, Dental, and Vision Insurance

AI Tech Lead

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Inorsa