About the role
As an Engineering and Customer facing leader, you will own the end-to-end design and delivery of AI-driven business transformation projects, while also innovating on tools and services on the DevRev product. You'll work closely with pre-sales teams to scope technical integration and implementation strategies, translating business requirements into architectural solutions. Once opportunities move to post-sales, your team will own the detailed technical designs from original scoping documents and drive execution to build proofs-of-concept, custom integrations, and solutions that validate technical feasibility.
What you'll do
- Customer-facing: Working 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.
What you'll bring
- Experience: 5+ years in software design and development, AI/ML engineering, or technical consulting coupled with customer-facing experience
- Leadership: 3+ years leading technology teams as a hands-on leader
- Customer Empathy: A bias for action and a relentless focus on solving problems for customers.
- Communication: Strong written and verbal skills to articulate technical concepts to both engineers and business stakeholders.
- Cloud & DevOps: Hands-on experience with AWS, GCP, or Azure, and modern DevOps practices (CI/CD, containers, observability).
- System Integration: Comfortable integrating systems via APIs, webhooks, and event/data pipelines.
- Data-Driven Mindset: Use A/B testing, telemetry, and metrics to guide decisions and drive improvements.
- Background: Bachelor's or Master’s degree in Computer Science, Engineering, or related discipline. Advanced degrees or certifications in AI/architecture frameworks (e.g., TOGAF, SAFe) are a plus
Nice to have
Coding Skills: Strong proficiency using TypeScript/JavaScript, Python, data structures and algorithms, (Nice to have: Go)
Applied AI Knowledge: Large language models (LLMs), prompt engineering, frameworks like RAG and function calling, and building evals to validate agentic AI solutions.
