5 months ago
Hyderābād, IndiaStaff+
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 builds a cloud platform for enterprise integration and workflow automation, combining iPaaS, no-code recipes, and AI to orchestrate processes across SaaS and on‑prem systems. It sells subscriptions to IT and operations teams at large and mid-size companies; customers include firms such as Nasdaq, Cisco, and Atlassian. Founded in 2013 and headquartered in Palo Alto, it is a privately held company.