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DevRev is building Computer, an AI teammate platform that unifies data sources, tools, and workflows into a single AI-ready system. The platform enables real-time insights, proactive suggestions, and agentic actions, extending existing software with AI-native apps that work alongside teams and customers.
This hybrid role sits at the intersection of solution design and hands-on delivery. You will own enterprise engagements end-to-end: conduct technical discovery, design solutions, build, deploy, and harden production agentic systems within customer environments. You are expected to design agent architecture on a whiteboard and debug production state machines—this is not an advisory role. Success is measured on working systems in production and measurable business outcomes.
Key responsibilities include:
**Discover & Design:** Lead technical discovery to map customer workflows, systems of record, and failure modes. Identify where agentic automation creates measurable economic impact (cost per case, resolution time, deflection). Build and deliver proofs of concept on live customer data. Translate business problems into agent architectures covering orchestration design, state management, tool/skill decomposition, guardrails, and escalation paths. Shape statements of work and delivery plans with defensible technical scope.
**Build & Deploy:** Implement production agents on the DevRev platform, including agent instructions, deterministic workflows, skills, and integrations with enterprise systems (CRM, ERP, payments, operational APIs). Own reliability by designing evaluation harnesses, debugging agent failures (hallucination, state drift, loops, guardrail leakage), and iterating to production-grade adoption. Drive integration work hands-on with REST APIs, webhooks, auth flows, and data mapping. Instrument and report outcomes including adoption, containment, accuracy, and cost metrics.
**Scale & Enable:** Convert engagement learnings into reusable assets: reference architectures, playbooks, sandbox environments, and internal tooling. Enable partner engineers and integrators to deliver independently through workshops and bootcamps. Feed structured field signal to Product on what breaks, what's missing, and competitive intelligence.
Required qualifications: 8–10 years in customer-facing technical roles (forward-deployed engineering, field engineering, solution delivery, technical specialist) in B2B SaaS or enterprise software. Hands-on experience building LLM-based systems in production: agent orchestration, prompt and context engineering, RAG, tool/function calling, and evaluation. You must have shipped something real with users and can articulate failure modes. Strong engineering fundamentals including proficiency in Python or JavaScript/TypeScript, fluency with REST APIs, webhooks, integration patterns, SQL, and working directly in customer data. Strong discovery and solutioning craft with stakeholder workshops, PoCs, and technical scoping. Executive-grade communication ability. Operates well in ambiguity with bias toward ownership.
Strong plus qualifications: Experience with deterministic workflow and state-machine design for AI agents, making non-deterministic systems reliable for regulated or high-volume operations. Production experience in complex enterprise domains (airlines, BFSI, telecom, manufacturing). Familiarity with CRM/CX platforms (Salesforce, Zendesk, ServiceNow), cloud platforms (AWS/Azure/GCP), and modern DevOps tooling. Exposure to AI-assisted development workflows (Cursor, Copilot, Claude Code).
