DevRev is hiring an Engineering Leader - Applied AI

About the Role

Role details below.

Responsibilities

• Work directly with customers
• Lead Execution: Define project plans, align internal teams, and ensure timely delivery of deployments
• Design & Deploy AI Agents: Build and configure intelligent solutions leveraging snap-ins, connectors (AirSync), workflows, and AI agents on the DevRev platform to solve real customer problems
• Integrate Systems: Connect AI agents with external platforms (e.g., CRMs, APIs, databases) for seamless workflow automation
• Optimize Performance: Tune prompts, logic, and agent configurations for accuracy, reliability, and scalability
• Own Requirements: Partner with customers to deeply understand their needs and translate them into technical agent specifications
• Prototype & Iterate: Lead live demos, build rapid proof-of-concepts, and refine solutions through customer feedback
• Advise Customers: Act as a trusted technical advisor on AI agent architecture, performance, and long-term scalability

Requirements

• 5+ years in software design and development, AI/ML engineering, or technical consulting coupled with customer-facing experience
• 3+ years leading technology teams as a hands-on leader
• A bias for action and a relentless focus on solving problems for customers
• Strong written and verbal skills to articulate technical concepts to both engineers and business stakeholders
• Hands-on experience with AWS, GCP, or Azure, and modern DevOps practices (CI/CD, containers, observability)
• Comfortable integrating systems via APIs, webhooks, and event/data pipelines
• Use A/B testing, telemetry, and metrics to guide decisions and drive improvements
• Bachelor's or Master’s degree in Computer Science, Engineering, or related discipline

Nice to Have

• Advanced degrees or certifications in AI/architecture frameworks (e.g., TOGAF, SAFe) are a plus
• Strong proficiency using TypeScript/JavaScript, Python, data structures and algorithms
• Nice to have: Go
• Large language models (LLMs), prompt engineering, frameworks like RAG and function calling, and building evals to validate agentic AI solutions

Additional Information

• Act as the connective tissue between pre-sales, customer success, engineering, and product teams
• Technical owner of the customer relationship
• Direct involvement in implementing key technical components and debugging complex integration challenges