Technical Support Engineer

• Serve as the first line of escalation for production issues in AI agentdriven loan origination and servicing flows.
• Debug API calls, frontend errors, and backend logs in MERN-based microservices.
• Trace data flow across React UI, Node.js backend, and database (MongoDB/Postgres).
• Analyse and resolve integration issues with third-party services (Plaid, DocuSign, Document AI, CRM, QuickBooks).

AI Agent & Workflow Support

• Monitor and support conversational AI agents (Google ADK / LangGraph / Vertex AI).
• Validate intent detection, conversation context, and state transitions in agent workflows.
• Assist in training and fine-tuning prompts or rules to improve AI performance.
• Collaborate with the AI engineering team to identify mis-routed or failed agent actions.

Operational Monitoring

• Review weekly dashboard logs (loan application success rates, failed API calls, stuck applications).
• Create automated alerts for loan status delays, underwriting bottlenecks, or data mismatches.
• Generate reports highlighting recurring issues and recommend preventive measures.

Collaboration

• Work with development teams to reproduce and isolate bugs.
• Partner with product managers and AI trainers to ensure fast recovery of workflows.
• Communicate fixes and known issues to internal and external stakeholders clearly.

Required Skills & Qualifications

• Bachelors degree in Computer Science, Engineering, or equivalent experience.
• 2 to 5 years of hands-on experience in technical support / product support / DevOps / full-stack debugging.
• Strong in JavaScript/TypeScript, Node.js, Express, React, MongoDB, and REST APIs.
• Experience with Postman, Docker, and Cloud (GCP/AWS) environments.
• Familiarity with AI agent frameworks (LangChain, LangGraph, Google ADK, or OpenAI functions).
• Knowledge of loan origination / banking / fintech domain preferred.
• Excellent problem-solving and communication skills.

Good-to-Have

• Understanding of prompt engineering or LLM-based workflow orchestration.
• Experience with QuickBooks, Plaid, DocuSign, or Document AI integrations.
• Familiarity with error monitoring tools (Sentry, Datadog, Cloud Logging).
• Knowledge of CI/CD pipelines and GitHub Actions for build & deployment debugging.

Performance Metrics

• Issue resolution time (SLA compliance)
• Number of first-time resolutions without escalation
• Reduction in repetitive support tickets
• Quality of documentation and knowledge-base contributions