DevRev

DevRev

Forward Deployed Architect

Austin, Texas, United States · Senior

Sponsorship not specified$30k-$50kDetected 67 days ago
JavaScriptTypeScriptPythonData StructuresAWSGCPAzureCI/CDDevOpsMachine LearningData EngineeringLLMsRAGAgentic AIA/B TestingCustomer SuccessCommunicationCollaboration

About the role

  • You will act as the connective tissue between pre-sales, customer success, engineering, and product teams, bringing our AI capabilities to life for real-world business impact.
  • The Applied AI Engineering team ensures our customers get the optimal experience from DevRev.
  • As customers go through their DevRev journey, they may identify needs for integration with existing enterprise systems and services, workflow and process automation, or customization of the DevRev platform to achieve their business objectives.

Responsibilities

  • Lead Execution: Define project plans, align internal teams, and ensure timely delivery of deployments
  • Design the detailed AI-driven business transformation, applied AI requirements, and architect the solution needed for customer needs
  • Create and maintain Applied AI proposals, estimates, solution designs, detailed requirements and measurement framework for KPIs and outcomes
  • Design & Deployment
  • 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
  • Optimize Performance: Tune prompts, logic, and agent configurations for accuracy, reliability, and scalability
  • Prototype & Iterate: Lead live demos, build rapid proof-of-concepts, and refine solutions through customer feedback

Requirements

  • Coding Skills: Strong proficiency using TypeScript/JavaScript, Python, data structures and algorithms.
  • Must be able to articulate AI build experiences, challenges, and trade-offs
  • Cloud & DevOps: Hands-on experience with AWS, GCP, or Azure at enterprise scale
  • Hands-on experience with AWS, GCP, or Azure at enterprise scale

Nice to have

  • 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
  • Background: Bachelor's or Master's degree in Computer Science, Engineering, or related discipline.

Skills

  • Strong proficiency using TypeScript/JavaScript, Python, data structures and algorithms.
  • 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 at enterprise scale; modern DevOps practices (CI/CD, containers, observability)
  • System Integration: Comfortable integrating systems via APIs, webhooks, and event/data pipelines
  • About DevRev

Compensation

  • $30k-$50k

Company info

  • Our team works with customers to understand requirements and design, develop, and implement solutions to meet customer goals.
  • Your mission is to systematically help customers find value with DevRev by developing a thorough understanding of their needs, owning coordination between internal and external stakeholders, and engineering the solution to get the job done.
  • You are a product expert and will use your application development and AI/ML skills to ensure our customers get the most out of the DevRev platform.
  • 30-50% working directly with customers
  • Act as a trusted technical advisor on AI agent architecture, performance, and long-term scalability
  • A bias for action and a relentless focus on solving problems for customers
  • Customer-Facing: 30-50% working directly with customers
  • Design solutions (AI Agents, Skills, and workflows, etc.) that are aligned with industry best practices, meet the customer needs, and are reusable across other customers
  • Own Requirements: Partner with customers to deeply understand their needs and translate them into technical agent specifications

This listing is sourced directly from DevRev's careers page and normalized into a canonical job model.