Technical Architect

Workato is looking for a Principal Technical Architect, AI to join as a Field Specialist operating at the intersection of applied AI engineering and customer-facing technical strategy. This is a dual-mandate role with an approximately equal split: Mandate 1 – AI Field Specialist (Customer-facing): You will work directly with enterprise customers and prospects in pre-sales and post-sales engagements to define and architect their AI strategies on Workato. This includes designing agentic automation solutions, advising on the latest approaches and best practices, scoping AI agent implementations, and helping customers move from proof-of-concept to production-grade autonomous systems. Mandate 2 – Agentic CS Platform (Internal initiative): You will be a core architect and developer on Workato's autonomous Customer Success agent initiative, a production-grade system that captures complete customer context to support reasoning, and compounds intelligence over time. You will own the agent and memory architecture, evals and learning loop, and autonomy framework that power this system. Responsibilities: AI Field Specialist - Support as an expert AI architect in strategic enterprise engagements: roadmapping customer AI agent use cases and delivering technical deep-dives. - Lead architecture workshops with enterprise customers to define AI automation strategy, agent design, integration patterns, and governance frameworks. - Advise customers on applied AI best practices: prompt engineering, agent orchestration, evaluation strategies, and human-in-the-loop design. - Build reusable technical assets: reference architectures, solution blueprints, and best-practice documentation. - Develop technical collateral: white papers, webinars, blog posts, and conference talks. - Partner with Product and Engineering to feed field insights back into the platform roadmap. - Support global strategic accounts across US, EMEA, and APAC (up to 20% global travel). Agentic CS Platform - Architect and implement core subsystems: memory layers, agent orchestration layer, and decision trace architecture. - Design and build a Customer Knowledge Graph as a structured, semantic network. - Implement the confidence-based autonomy framework, including precedent matching and confidence scoring. - Define infrastructure architecture for optimal performance and latency. - Take scalable technology selection decisions. - Establish evaluation frameworks for decision quality and learning velocity. Requirements: - B.Tech/BE or higher in Computer Science, Engineering, or related field. - 15+ years of total relevant experience in enterprise software architecture, design, and implementation. - 8+ years of hands-on experience with Integration Platforms (MuleSoft, TIBCO, Oracle SOA, webMethods, or similar). - 2+ years of applied AI/Agents engineering experience with LLMs and agent frameworks. - Hands-on experience with AI agent systems: multi-step reasoning pipelines and tool-use orchestration. - Working knowledge of Python agent frameworks (LangGraph, Claude Agent SDK, etc.). - Experience with graph database design (Neo4j/NetworkX) and RAG architectures. - Understanding of evaluation and testing for AI systems and prompt engineering at production scale. - Experience with observability for LLM systems (Langfuse, LangSmith, Phoenix) and MCP standards. - Expertise in enterprise integration patterns, APIs (RESTful, SOAP), and cloud platforms (AWS, Azure, or GCP). - Familiarity with enterprise applications (Salesforce, ServiceNow, SAP, etc.) and security/governance (OAuth, SSO, SOX).