Software Engineer (Backend / AI)

About Us

Raven is a YC-backed startup (S22) building AI assistants for manufacturing plants. We’re taking decades of manufacturing expertise and combining it with AI to solve real operational problems. Backed by top VCs, we’re a small, focused team working to make industrial operations safer, smarter, and more efficient. We’re based in Bangalore, building for teams that work in some of the toughest environments.

What You’ll Work On

This is a core technical role focused on building AI-native applications and agents for manufacturing workflows. You’ll:

• Build Python/Go backend services that integrate tightly with AI systems.
• Help improve multi-modal pipeline infrastructure that handle P&IDs, SOPs, sensor data, and technical documents.
• Build agent memory systems using knowledge graphs and event timelines.
• Architect AI orchestration layers for decision making workflows from structured/unstructured plant data.
• Rapidly prototype new AI workflows and deploy in real-world plant environments.

Who We’re Looking For

• 2–4+ years experience building production systems.
• Strong Python/Go skills, familiar with LLMs, embeddings, vector stores.
• Interest in owning problems and figuring out solutions
• Deep interest in owning problems end-to-end—from exploring the solution space to deploying working systems in production.
• Comfortable working in ambiguity, iterating fast, and shipping real-world value.

Bonus: You’ve worked in a startup before and are comfortable wearing multiple hats.

Why Join Us

We’re not just building one-off tools—we’re building core systems that empower plant teams to make faster, safer decisions. This is about creating systems that process complex, scattered information and make it accessible in seconds.

Here’s what we offer:

Impact: Be one of the first hires and shape the product, culture, and direction of the company. Ownership: We’re offering 0.1-1% equity because this should feel like your company too.

Focus: This is a chance to work on deep, meaningful problems that directly affect real-world outcomes in safety and efficiency.

We believe in working together in person, especially at this stage of the company. Fast iteration and collaboration matter, and being in the room together makes a difference.