Role Overview

We are looking for domain-first, systems-oriented engineers who understand how real-world systems (in renewable energy, automotive, manufacturing, IoT, etc.) generate and use data — and can translate that into meaningful AI-driven workflows using DevRev.

As a Solutions Engineer, you will act as a trusted advisor, combining domain expertise, engineering depth, and problem-solving to help customers unlock value from their data through DevRev’s AI platform.

You will work closely with Sales, Product, and Engineering to design solutions that go beyond demos — enabling real-world automation, intelligence, and agentic workflows.

 

Key Responsibilities

• Partner with customers to deeply understand domain workflows, systems, and data flows (e.g., connected vehicles, factory systems, IoT environments)
• Translate real-world signals (sensor data, logs, events) into insights, workflows, and automated actions
• Build and showcase AI-driven workflows, automations, and agentic use cases
• Design and articulate end-to-end solutions (data ingestion → processing → decision → action)
• Act as a technical advisor, guiding customers on how DevRev can integrate into and enhance their existing systems
• Deliver tailored demos, PoCs, and solution walkthroughs grounded in real-world use cases (not generic SaaS demos)
• Support integrations with external systems via APIs, webhooks, and data pipelines
• Contribute to technical proposals, RFP/RFI responses, and solution architectures
• Stay current on industry trends (renewable energy, automotive, manufacturing, IoT, AI) and bring insights into customer conversations

 

Qualifications & Skills

Domain Expertise

• Experience in renewable energy, automotive, manufacturing, industrial systems, or IoT environments
• Strong understanding of:
• Connected systems (vehicles, machines, robotics, embedded systems)
• Sensor ecosystems (CAN, telemetry, vision systems, IoT devices)
• Real-world data (signals, logs, time-series)
• Familiarity with manufacturing workflows (inspection, defect detection, production cycles).
• Ability to explain data flow from system → processing → business outcomes

Engineering Depth

• Strong system-level thinking
• Experience with:
• Data pipelines, APIs, integrations, distributed systems
• Experience in Python / TypeScript / JavaScript (or similar)
• Understanding of system design, reliability, and failure handling
• Exposure to AI/ML in industrial use cases (vision systems, predictive maintenance, log-stream analysis, etc.)

Problem Solving & Use Case Thinking

• Ability to translate data → insights → workflows → actions
• Experience designing end-to-end systems
• Strong analytical mindset to identify automation opportunities from real-world signals

AI & Tools

• Exposure to LLMs, RAG, AI agents
• Familiarity with prompt design and AI workflows
• Tools: Postman, GitHub, Jira, Cursor, Claude Code