Asks for

The ad says“Deep working knowledge of APIs: RESTful, SOAP, webhooks, and data formats (JSON, XML).”
The ad says“2+ years of applied AI/Agents engineering experience, specifically in building systems that leverage LLMs and agent frameworks, orchestrating them into production applications.”
The ad says“Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native services (Lambda/Functions, DynamoDB, Kafka/Kinesis, S3).”
The ad says“Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native services (Lambda/Functions, DynamoDB, Kafka/Kinesis, S3).”
The ad says“Working knowledge of Python agent frameworks such as LangGraph, Claude Agent SDK, or equivalent. LangGraph experience with persisted state and human-in-the-loop patterns is strongly preferred.”
The ad says“Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native services (Lambda/Functions, DynamoDB, Kafka/Kinesis, S3).”
The ad says“Strong expertise in enterprise integration patterns: event-driven architectures, microservices orchestration, pub/sub messaging, and data synchronization.”
The ad says“Understanding of enterprise security and governance requirements: OAuth, SSO, data residency, SOX compliance, audit trails, and role-based access control.”
The ad says“15+ years of total relevant experience in enterprise software architecture, design, and implementation.”
The ad says“Strong expertise in enterprise integration patterns: event-driven architectures, microservices orchestration, pub/sub messaging, and data synchronization.”
The ad says“Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native services (Lambda/Functions, DynamoDB, Kafka/Kinesis, S3).”
The ad says“Experience with graph database design and implementation: entity modeling, relationship extraction, graph querying, and using graph structures for contextual retrieval. Neo4j/NetworkX equivalent experience is a plus.”
The ad says“8+ years of hands-on experience with Integration Platforms (MuleSoft, TIBCO, Oracle SOA, webMethods, or similar). Deep familiarity with iPaaS architecture patterns is essential.”
The ad says“8+ years of hands-on experience with Integration Platforms (MuleSoft, TIBCO, Oracle SOA, webMethods, or similar). Deep familiarity with iPaaS architecture patterns is essential.”
The ad says“Experience with observability for LLM systems: tracing, token/cost monitoring, and latency profiling. Familiarity with Langfuse, LangSmith, Phoenix, or similar tools.”
The ad says“Working knowledge of Python agent frameworks such as LangGraph, Claude Agent SDK, or equivalent. LangGraph experience with persisted state and human-in-the-loop patterns is strongly preferred.”
The ad says“Experience with observability for LLM systems: tracing, token/cost monitoring, and latency profiling. Familiarity with Langfuse, LangSmith, Phoenix, or similar tools.”
The ad says“2+ years of applied AI/Agents engineering experience, specifically in building systems that leverage LLMs and agent frameworks, orchestrating them into production applications.”
The ad says“Experience with MCP (Model Context Protocol) standards for LLM-to-system integration.”
The ad says“Strong expertise in enterprise integration patterns: event-driven architectures, microservices orchestration, pub/sub messaging, and data synchronization.”
The ad says“Experience with graph database design and implementation: entity modeling, relationship extraction, graph querying, and using graph structures for contextual retrieval. Neo4j/NetworkX equivalent experience is a plus.”
The ad says“Understanding of enterprise security and governance requirements: OAuth, SSO, data residency, SOX compliance, audit trails, and role-based access control.”
The ad says“Experience with observability for LLM systems: tracing, token/cost monitoring, and latency profiling. Familiarity with Langfuse, LangSmith, Phoenix, or similar tools.”
The ad says“Experience with observability for LLM systems: tracing, token/cost monitoring, and latency profiling. Familiarity with Langfuse, LangSmith, Phoenix, or similar tools.”
The ad says“Familiarity with prompt engineering at production scale: structured prompting, chain-of-thought patterns, output parsing, retry/fallback strategies, and prompt versioning.”
The ad says“Strong expertise in enterprise integration patterns: event-driven architectures, microservices orchestration, pub/sub messaging, and data synchronization.”
The ad says“Working knowledge of Python agent frameworks such as LangGraph, Claude Agent SDK, or equivalent. LangGraph experience with persisted state and human-in-the-loop patterns is strongly preferred.”
The ad says“Practical experience with RAG architectures, vector databases, embedding strategies, and hybrid retrieval (vector + structured + graph).”
The ad says“Deep working knowledge of APIs: RESTful, SOAP, webhooks, and data formats (JSON, XML).”
The ad says“Understanding of enterprise security and governance requirements: OAuth, SSO, data residency, SOX compliance, audit trails, and role-based access control.”
The ad says“Deep working knowledge of APIs: RESTful, SOAP, webhooks, and data formats (JSON, XML).”
The ad says“Understanding of enterprise security and governance requirements: OAuth, SSO, data residency, SOX compliance, audit trails, and role-based access control.”
The ad says“Understanding of enterprise security and governance requirements: OAuth, SSO, data residency, SOX compliance, audit trails, and role-based access control.”
The ad says“Practical experience with RAG architectures, vector databases, embedding strategies, and hybrid retrieval (vector + structured + graph).”

About Workato

Workato is the leading Control and Execution Platform for Enterprise AI — the neutral platform enterprises trust to put AI to work across their business. Workato unifies data, applications, and processes into a single platform so AI can reliably orchestrate business processes in production at enterprise scale. Built on more than a decade of running mission-critical processes for over half the Fortune 500 — including Nasdaq, Amazon, Cisco, Vodafone, Atlassian, and Lucid Motors — Workato turns over 14,000 enterprise systems AI needs to act on into one governed execution layer. For more information, visit workato.com.

Why join us?

Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.

But, we also believe in balancing productivity with self-care. That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.

If this sounds right up your alley, please submit an application. We look forward to getting to know you!

Also, feel free to check out why:

Business Insider named us an “enterprise startup to bet your career on”
Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world
Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
Quartz ranked us the #1 best company for remote workers

About the Role

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.

This is a role for someone who builds agentic systems to orchestrate LLMs into reliable, auditable, enterprise-grade agent architectures, and who can articulate that vision to a customer CIO and technical leaders as fluently as they can implement it in code.

Workato has a positive, diverse, and collaborative culture; we look for people who are curious, inventive, smart, hardworking, and who work to be a little better every day.

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 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 and agent orchestration patterns, evaluation and testing strategies for agent systems, 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, that enable the broader field team to position AI solutions.
Develop and deliver technical collateral: architecture white papers, webinars, blog posts, conference talks, etc. that establish Workato's thought leadership in agentic automation.
Partner with Product and Engineering to feed field insights back into the platform roadmap, ensuring customer-facing AI capabilities evolve based on real deployment patterns.
Support global strategic accounts across the US, EMEA, and APAC geographies as needed for critical engagements.
May require up to 20% global travel.

Agentic CS Platform

Architect and implement core subsystems of the autonomous CS agent platform, including memory layers, the agent orchestration layer, and the decision trace architecture.
Design and build a Customer Knowledge Graph as a structured, semantic network that models customers, users, policies, decisions, and operational artifacts as interconnected entities and relationships, enabling queryable representation of contextual states, and policy evaluations for governance, traceability, and decision optimization.
Implement the confidence-based autonomy framework, including 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.
Take scalable technology selection decisions.
Establish evaluation frameworks: define metrics for decision quality, context accuracy, and learning velocity (autonomous threshold increases month over month).

Requirements

Qualifications & Experience

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). Deep familiarity with iPaaS architecture patterns is essential.
2+ years of applied AI/Agents engineering experience, specifically in building systems that leverage LLMs and agent frameworks, orchestrating them into production applications.

Applied AI & Agent Architecture (Must-Have)

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. LangGraph experience with persisted state and human-in-the-loop patterns is strongly preferred.
Experience with graph database design and implementation: entity modeling, relationship extraction, graph querying, and using graph structures for contextual retrieval. Neo4j/NetworkX equivalent experience is a plus.
Practical experience with RAG architectures, vector databases, embedding strategies, and hybrid retrieval (vector + structured + graph).
Understanding of evaluation and testing for AI systems: building evals, measuring agent quality, confidence calibration, A/B testing of prompts/pipelines, and regression testing for non-deterministic outputs.
Familiarity with prompt engineering at production scale: structured prompting, chain-of-thought patterns, output parsing, retry/fallback strategies, and prompt versioning.
Experience with observability for LLM systems: tracing, token/cost monitoring, and latency profiling. Familiarity with Langfuse, LangSmith, Phoenix, or similar tools.
Experience with MCP (Model Context Protocol) standards for LLM-to-system integration.

Integration & Enterprise Architecture (Must-Have)

Strong expertise in enterprise integration patterns: event-driven architectures, microservices orchestration, pub/sub messaging, and data synchronization.
Deep working knowledge of APIs: RESTful, SOAP, webhooks, and data formats (JSON, XML).
Experience with cloud platforms (AWS, Azure, or GCP) and cloud-native services (Lambda/Functions, DynamoDB, Kafka/Kinesis, S3).
Familiarity with enterprise applications: Salesforce, ServiceNow, SAP, Workday, NetSuite, or similar. Understanding how these systems participate in agentic workflows.
Understanding of enterprise security and governance requirements: OAuth, SSO, data residency, SOX compliance, audit trails, and role-based access control.

Nice-to-Have

Background in Customer Success platforms (Gainsight, ChurnZero) or CRM architecture.
Prior experience in a customer-facing technical role (Solutions Architect, Field CTO, Technical Account Manager, or Pre-Sales Engineer) at a SaaS or platform company.
Contributions to open-source AI/agent projects or published technical content in the applied AI space.

Soft Skills

Exceptional ability to communicate complex AI and architecture concepts to diverse audiences: from engineering teams to C-level executives: adapting depth and vocabulary to the room.
Strong consultative instinct: able to listen to a customer's business problem, decompose it into architectural components, and propose a credible path forward in real-time.
Entrepreneurial mindset with comfort operating in ambiguity: this role spans an internal build and an external advisory function, and requires someone who can context-switch and self-prioritize.
Collaborative by default: able to work across Product, Engineering, Customer Success, Sales, and Marketing without needing formal authority.
Excellent written communication: able to produce architecture documents, white papers, and technical briefs that are clear, precise, and persuasive.
Fast learner who stays current with the rapidly evolving AI agent ecosystem and can distill emerging patterns into practical guidance.