3 months ago
Responsibilities
- Advise enterprise customers and prospects on AI agent use cases, strategies, architectures, and production implementations.
- Lead customer architecture workshops covering agent design, integration patterns, governance, evaluation, confidence calibration, human-in-the-loop workflows, and continuous learning.
- Build reference architectures, solution blueprints, demonstration environments, and best-practice documentation for the field team.
- Create technical collateral including architecture white papers, webinars, blog posts, and conference presentations.
- Partner with Product and Engineering to incorporate customer insights into the AI platform roadmap.
- Architect and implement memory, agent orchestration, decision trace, and autonomy subsystems for the autonomous Customer Success agent platform.
- Design and build a Customer Knowledge Graph representing customers, users, policies, decisions, and operational artifacts for contextual retrieval and governance.
- Implement confidence scoring, precedent matching, policy-governed escalation routing, and the autonomy framework.
- Define infrastructure architecture and scalable technology selections for performance and latency.
- Establish evaluation frameworks and metrics for decision quality, context accuracy, and learning velocity.
- Support strategic accounts across US, EMEA, and APAC geographies as needed.
Requirements
- B.Tech/BE or higher in Computer Science, Engineering, or a related field.
- 15+ years of relevant experience in enterprise software architecture, design, and implementation.
- 8+ years of hands-on experience with integration platforms such as MuleSoft, TIBCO, Oracle SOA, or webMethods.
- At least 2 years of applied AI or agent engineering experience building production applications that leverage LLMs and agent frameworks.
- Hands-on experience designing AI agent systems with multi-step reasoning, tool-use orchestration, and autonomous execution.
- Working knowledge of Python agent frameworks such as LangGraph or Claude Agent SDK; persisted state and human-in-the-loop experience is preferred.
- Experience designing and implementing graph databases, entity models, relationship extraction, graph querying, and contextual retrieval; Neo4j or NetworkX experience is a plus.
- Practical experience with RAG architectures, vector databases, embeddings, and hybrid retrieval.
- Experience evaluating and testing AI systems, including agent quality metrics, confidence calibration, prompt or pipeline A/B testing, and regression testing.
- Production-scale prompt engineering experience, including structured prompts, output parsing, retry and fallback strategies, and prompt versioning.
- Experience with LLM observability, including tracing, token and cost monitoring, and latency profiling; Langfuse, LangSmith, or Phoenix familiarity is a plus.
- Experience with MCP standards for LLM-to-system integration.
- Strong expertise in enterprise integration patterns, including event-driven architectures, microservices orchestration, pub/sub messaging, and data synchronization.
- Deep knowledge of RESTful and SOAP APIs, webhooks, JSON, and XML.
- Experience with AWS, Azure, or GCP and cloud-native services such as Lambda or Functions, DynamoDB, Kafka or Kinesis, and S3.
- Familiarity with enterprise applications including Salesforce, ServiceNow, SAP, Workday, and NetSuite.
- Understanding of enterprise security and governance requirements including OAuth, SSO, data residency, SOX compliance, audit trails, and role-based access control.
- Customer Success platform or CRM architecture experience, including Gainsight or ChurnZero, is preferred.
- Prior customer-facing technical experience as a Solutions Architect, Field CTO, Technical Account Manager, or Pre-Sales Engineer is preferred.
- Contributions to open-source AI or agent projects or published technical content in applied AI are preferred.
- Strong communication, consultative problem-solving, collaboration, written communication, adaptability, and ability to explain complex AI and architecture concepts to technical and executive audiences.
Benefits
- Flexible, trust-oriented work culture with employee ownership.
- Vibrant and dynamic work environment with multiple employee benefits.
- Remote-friendly work environment.
- Role may require up to 20% global travel and support across US, EMEA, and APAC.
Tech Stack
Categories
AI ApplicationsSolutions Engineering
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. We connect to every app, system, and process your business runs on, with no data migration, no rip-and-replace, 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 14,000+ enterprise systems into one governed execution layer. AI has solved reasoning. The next frontier is execution, with Workato.