AI Software Engineer

About Seeq

Seeq builds advanced analytics software for customers in process manufacturing industries such as pharmaceuticals, mining, renewables, and energy — organizations with massive amounts of time-series and event data from industrial operations.

We are a fully remote company that has operated this way from the start, using tools like Zoom, Slack, and our homegrown Qube virtual office to collaborate as if we were in the same building. Our teams iterate using agile practices and care deeply about clear communication, shared learning, and delivering software that exceeds customer expectations.

Role Overview

The AI Software Engineer is a domain-leading individual contributor who helps define and scale the backend platforms and AI systems supporting Seeq’s intelligent applications.

You will own highly ambiguous, cross-team technical initiatives and provide architectural leadership for backend platforms and agentic systems, helping establish the shared infrastructure, services, APIs, and engineering patterns that allow AI-enabled capabilities to operate reliably and safely at scale across Seeq.

This is a deeply hands-on role. You will combine significant experience building AI systems with strong backend and distributed systems expertise, broad technical influence, and continued involvement in designing, coding, debugging, and shipping production software.

Key Responsibilities

• Set technical direction for backend and AI platform capabilities: Define architecture, patterns, and technical approaches for scalable systems supporting AI and agentic applications.
• Lead high-impact AI and platform initiatives: Own complex, ambiguous projects from concept through production, coordinating work across engineers, teams, and stakeholders.
• Architect agentic systems: Design and build systems supporting agent routing, orchestration, tool use, evaluations, runtime infrastructure, sandboxing, and agent-to-agent communication.
• Build and evolve shared platform infrastructure: Define backend services, runtime environments, interfaces, and architectural patterns that other engineering teams rely on to build AI-enabled applications.
• Build reliable distributed and data-intensive systems: Develop backend services and APIs capable of supporting AI workloads across large data volumes and complex workflows.
• Create reusable AI platform capabilities: Build shared services, APIs, libraries, orchestration patterns, runtime infrastructure, and developer-facing platform capabilities that enable multiple teams to build, deploy, and operate AI features consistently.