Key Responsibilities
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 AnalysisDiagnose, 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 EnablementBuild 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 AutomationWrite 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
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, Lambada, 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.
Skills Required
- 1-3 years L2 technical support experience in enterprise SaaS platforms (AWS cloud preferred)
- Proven track record handling technical issues in production distributed systems and multi-tenant SaaS
- Hands-on experience troubleshooting AI-powered product features and distinguishing model vs data/config/system issues
- Experience with SSO protocols (SAML, OAuth 2.0, OpenID Connect), IAM, MFA, RBAC, JIT provisioning and SCIM
- Proficient in SQL for querying and analysis; experience with PostgreSQL, MySQL and Snowflake
- Experience querying and analyzing logs and observability data (Datadog, Loki, SumoLogic, Splunk, Grafana)
- Proficient in debugging backend services and APIs (GoLang or Python; REST, GraphQL, gRPC)
- Familiarity with AWS services (Lambda, CloudWatch, S3), Kafka and Kubernetes
- Proficient in debugging React frontend apps and capturing/analyzing HAR files, browser dev tools and network traces
- Experience with user/session replay and BI tools (Zipy, FullStory, Mixpanel, Metabase)
- Ability to write scripts and automation using Bash, Python and SQL for diagnostics and health checks
- Strong written and verbal communication, customer empathy, and ability to manage SLAs and prioritized incidents
- Familiarity with prompt engineering and AI agent workflows
- Experience troubleshooting mobile apps on Android and iOS
What We Do
Mindtickle provides a comprehensive, data-driven solution for sales readiness and enablement that fuels revenue growth and brand affinity. Its purpose-built applications, proven methodologies, and best practices are designed to drive effective sales onboarding and ongoing readiness. With Mindtickle, revenue and sales leaders can continually assess, diagnose and develop the knowledge, skills, and behaviors required to effectively engage buyers and drive growth. Companies across a wide range of industries use Mindtickle's innovative capabilities for onboarding, training, bite-sized mobile updates, gamification-based learning, call recording, coaching and role-play to ensure world-class sales performance.
