About the role
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