Principal Technical Architect

Workato, Inc.

Job Overview

Location

Hyderabad, India

Job Type

Full-time

Full Job Description

📋 Description

• Serve as a Principal Technical Architect, AI, operating at the intersection of applied AI engineering and customer-facing technical strategy with equal focus on field specialization and internal platform development.
• Act as an expert AI architect in strategic enterprise engagements, roadmapping customer AI agent use cases and delivering technical deep-dives that demonstrate Workato’s agentic capabilities.
• Lead architecture workshops with enterprise customers to define their AI automation strategy, including agent design, integration patterns, and governance frameworks.
• Advise customers on applied AI best practices: prompt engineering, agent orchestration patterns, evaluation and testing strategies, confidence calibration, human-in-the-loop design, and continuous learning loops.
• Build reusable technical assets such as reference architectures, solution blueprints, demonstration environments, and best-practice documentation to enable the broader field team to position AI solutions.
• Develop and deliver technical collateral including architecture white papers, webinars, blog posts, and conference talks to establish Workato’s thought leadership in agentic automation.
• Partner with Product and Engineering teams to feed field insights into the platform roadmap, ensuring AI capabilities evolve based on real-world deployment patterns.
• Support global strategic accounts across the US, EMEA, and APAC regions as needed for critical engagements, with up to 20% global travel required.
• Architect and implement core subsystems of Workato’s autonomous Customer Success agent platform, including memory layers, agent orchestration layer, and decision trace architecture.
• Design and build a Customer Knowledge Graph as a structured, semantic network modeling customers, users, policies, decisions, and operational artifacts as interconnected entities to enable contextual retrieval and policy evaluations.
• Implement a confidence-based autonomy framework featuring precedent matching, confidence scoring (precedent match, pattern recognition, data completeness, policy clarity), and policy-governed escalation routing.
• Define and design infrastructure architecture to ensure optimal performance and latency for production-grade AI agent systems.
• Make scalable technology selection decisions for agent platform components including data storage, retrieval, and orchestration layers.
• Establish evaluation frameworks with metrics for decision quality, context accuracy, and learning velocity, tracking autonomous threshold improvements month-over-month.
• Apply hands-on expertise in designing AI agent systems with multi-step reasoning pipelines, tool-use orchestration, and autonomous execution frameworks.
• Utilize Python-based agent frameworks such as LangGraph, Claude Agent SDK, or equivalents, with strong preference for LangGraph experience involving persisted state and human-in-the-loop patterns.
• Apply practical experience with RAG architectures, vector databases, embedding strategies, and hybrid retrieval combining vector, structured, and graph data.
• Implement evaluation and testing methodologies for AI systems including evals, agent quality measurement, confidence calibration, A/B testing of prompts/pipelines, and regression testing for non-deterministic outputs.
• Apply production-scale prompt engineering techniques including structured prompting, chain-of-thought patterns, output parsing, retry/fallback strategies, and prompt versioning.
• Implement observability for LLM systems using tracing, token/cost monitoring, and latency profiling, with familiarity in tools like Langfuse, LangSmith, or Phoenix.
• Apply knowledge of MCP (Model Context Protocol) standards for LLM-to-system integration.
• Demonstrate strong expertise in enterprise integration patterns: event-driven architectures, microservices orchestration, pub/sub messaging, and data synchronization.
• Apply deep working knowledge of APIs including RESTful, SOAP, webhooks, and data formats (JSON, XML).
• Leverage experience with cloud platforms (AWS, Azure, GCP) and cloud-native services such as Lambda/Functions, DynamoDB, Kafka/Kinesis, and S3.
• Understand enterprise applications including Salesforce, ServiceNow, SAP, Workday, and NetSuite, and how they integrate into agentic workflows.
• Apply understanding of enterprise security and governance requirements: OAuth, SSO, data residency, SOX compliance, audit trails, and role-based access control.
• Communicate complex AI and architecture concepts fluently to diverse audiences—from engineering teams to C-level executives—adapting depth and vocabulary as needed.
• Demonstrate strong consultative instincts by listening to customer business problems, decomposing them into architectural components, and proposing credible technical paths forward in real time.
• Operate with an entrepreneurial mindset in ambiguity, self-prioritizing across internal platform development and external customer advisory responsibilities.
• Collaborate effectively across Product, Engineering, Customer Success, Sales, and Marketing teams without formal authority.
• Produce clear, precise, and persuasive written documentation including architecture documents, white papers, and technical briefs.
• Stay current with the rapidly evolving AI agent ecosystem and distill emerging patterns into practical, actionable guidance for internal and external stakeholders.

🎯 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, specifically in building systems that leverage LLMs and agent frameworks
• Hands-on experience designing and building AI agent systems: multi-step reasoning pipelines, tool-use orchestration, and autonomous execution frameworks
• Working knowledge of Python agent frameworks such as LangGraph, Claude Agent SDK, or equivalent

🏖️ Benefits

• Flexible, trust-oriented culture that empowers full ownership of roles
• Balance of productivity with self-care supported through company policies
• Vibrant and dynamic work environment
• Opportunity to work with 50% of the Fortune 500
• Global travel opportunities (up to 20%)
• Exposure to cutting-edge AI agent platform development at a Forbes Cloud 100 company

Skills & Technologies

Python

DynamoDB

Neo4j

AWS

Azure

Senior

Remote

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Workato, Inc.

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About Workato, Inc.

Workato provides low-code/no-code enterprise automation and integration software that connects applications, data, and business processes across cloud and on-premises systems. Its platform offers pre-built connectors, recipes, and AI-powered workflow orchestration for finance, HR, IT, sales, support, and marketing functions. The company enables organizations to automate tasks without extensive coding, reducing manual effort and accelerating digital transformation initiatives. Workato serves mid-market to large enterprises worldwide through a subscription-based SaaS model, emphasizing security, governance, and scalability for complex integrations.

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