Key Responsibilities

  • Operational Support and Issue Ownership
  • Respond rapidly to customer queries and drive issues to resolution.
  • Provide timely workarounds and temporary solutions to customers for known issues, ensuring minimal disruption while long-term fixes are being implemented.
  • Own support tickets end-to-end, ensuring SLA compliance and serving as an escalation point.
  • Work cross-functionally with Product, Engineering, QE, and DevOps to eliminate systemic issues and reduce ticket volume.
  • Work independently with strong planning and task management skills.
  • Use data analysis to make actionable, data-driven recommendations.
  • Technical Investigation, Diagnostics, and Root Cause Analysis
  • Diagnose, reproduce, and debug complex issues across backend services, APIs, data flows, UI behavior, and AI-driven components.
  • Capture and analyze browser logs, HAR files, network traces, and client-side errors using modern observability tools.
  • Analyze AI-driven decision paths, validate model outputs, and identify anomaly or drift patterns.
  • Troubleshoot integrations with external systems, authentication flows, and third-party platforms.
  • Leverage tools such as Zipy, DataDog, SumoLogic, FullStory, Mixpanel, and Metabase for deep diagnostics.
  • Identify, diagnose, and surface real-world failure modes in AI workflows, partnering with Product and Engineering to address root causes and deliver more resilient, trustworthy AI experiences.
  • Provide configuration guidance and technical solutions to resolve customer issues across environments.
  • Perform structured impact and severity analysis before escalating to Engineering.
  • Establish repeatable auditing frameworks to bring consistency to investigations.
  • Knowledge Building, Documentation and Enablement
  • Build and maintain high-quality runbooks, playbooks, troubleshooting guides, and diagnostic frameworks to reduce repeat incidents and improve response speed.
  • Convert solved issues into product and documentation improvements for L1, customers, Product, and Engineering.
  • Drive internal knowledge sharing by conducting deep dive sessions and post-incident walkthroughs.
  • Communicate solutions clearly to customers and internal teams, translating complex root causes into simple explanations.
  • Scripting, Tooling, and Automation
  • Write scripts (Bash/Python/SQL) to automate log extraction, data analysis, replication of issues, and health checks.
  • Create lightweight internal utilities to accelerate diagnostics and reduce manual investigation time.
  • Contribute to building support automation pipelines and AI-assisted troubleshooting tools.

Requirements

  • Experience and Background:
  • 1-3 years L2 technical support experience in enterprise SaaS platforms/products (AWS Cloud preferred) in a customer-facing role.
  • Proven track record handling technical issues in production environments, supporting distributed systems.
  • Experience in supporting a multi-tenant SaaS product or platform; familiarity with multi‑tenant architecture, Integrations and configuration management.
  • Troubleshooting AI-powered Product features
  • Hands-on experience troubleshooting AI-powered features. Able to distinguish model issues from data, configuration, or system-level failures with precision.
  • Knowledge of Authentication, Authorization, and Enterprise Integrations
  • SSO protocols (SAML, OAuth 2.0, OpenID Connect).
  • Identity and Access Management (IAM) frameworks.
  • Multi-Factor Authentication (MFA) and Role-Based Access Control (RBAC)
  • Just-in-time (JIT) provisioning and SCIM (System for Cross-domain Identity Management).
  • Backend and Systems skills:
  • Strong understanding of database concepts; proficient in SQL for querying, analysis, and data validation. Experience with relational and NoSQL systems (e.g., PostgreSQL, MySQL) and ability to debug data inconsistencies and performance issues, and hands-on experience in Snowflake.
  • Experience querying and analyzing logs, identifying patterns (tools like Datadog, Loki, Sumologic, Splunk, Grafana).
  • Proficient in debugging modern web applications across backend (GoLang/Python) and API layers (REST, GraphQL; gRPC).
  • Familiarity with AWS services, including Lambda, CloudWatch, S3 as well as experience with Kafka, Kubernetes, and Snowflake.
  • Frontend and client-side skills:
  • Proficient in debugging React applications.
  • Capture and analyze client logs (HAR files, network waterfalls, browser dev tools, net-export, wireshark).
  • Experience with monitoring tools (Zipy, Fullstory) and BI/analytics tools (Mixpanel, Metabase).
  • Customer Relationship and Soft Skills:
  • Strong written, verbal communication skills and empathy when interacting with customers.
  • Ability to build and maintain trusted relationships with customers and internal teams.
  • Maintains a calm and methodical approach in high-pressure situations.
  • Proactive, detail-oriented, ​​thrives in ambiguity, adapts quickly to changing priorities, and manages multiple concurrent issues in a fast-paced environment.
  • Analyzes and prioritizes issues based on SLAs and customer impact.

Good to have:

  • Familiarity with prompt engineering (helpful for L2 support on AI-powered products).
  • Understanding of building or configuring AI agents, including workflows, task automation, and basic agent behavior troubleshooting.
  • Experience in Mobile App troubleshooting on Android and iOS.

Job Summary

CompanyMindtickle
LocationPune, Maharashtra
TypeFull-Time
LevelMid-level
DomainSupport / Customer Success