About Workato

Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration Workato helps businesses globally streamline operations by connecting data processes applications and experiences. Its AI-powered platform enables teams to navigate complex workflows in real-time driving efficiency and agility.

Trusted by a community of 400000 global customers Workato empowers organizations of every size to unlock new value and lead in todays fast-changing world. Learn how Workato helps businesses of all sizes achieve more at .

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. Thats 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 Workatos 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 Workatos 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 Workatos 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

• 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.

General 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 customers 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.

(REQ ID: 2216)