Engineering Leader - Applied AI | DevRev · Teeming.ai
DevRev
AI is changing the way we work. At DevRev, we’re making it conversational.
DevRev’s AI-native business operating system designed for you to experience conversational AI at work—transforming complex…
AI is changing the way we work. At DevRev, we’re making it conversational.
DevRev’s AI-native business operating system designed for you to experience conversational AI at work—transforming complex…
Product: AI-native business OS called Computer (conversational AI teammate)
Valuation: $1.15B (reported)
Known investors: Khosla Ventures, Mayfield Fund, Param Hansa Values
Company Overview
Problem Domain
Unifying customer support, product development, and operational data; automating workflows via conversational AI.
Founded
2020
Industry
Software Development
Funding Track Record
Seed- July 2021
$50,000,000
Reported seed round announced at launch
Series A- August 2024
$100,800,000
Reported Series A that brought valuation to ~$1.1B–$1.15B; investors named include Khosla Ventures, Mayfield Fund, Param Hansa Values
Investor Signal
“Backed by venture firms including Khosla Ventures, Mayfield Fund, and Param Hansa Values”
Founders
What we do
Join the Team
Engineering Leader - Applied AI
HybridBengaluru, Karnataka, IN
Hybrid • Bengaluru, Karnataka, IN
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
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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.
