Technical Support Engineer - L2
jobgether · India ·
- Work mode
- Remote
- Employment
- Full time
- Category
- Support
- Experience
- 5+ years
jobgether · India ·
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Technical Support Engineer - L2 based in India.
This is a senior technical support role focused on resolving complex, business-critical issues across conversational AI and enterprise SaaS environments. You will own escalated cases from initial investigation through root-cause analysis, resolution, and customer confirmation. The role combines hands-on troubleshooting across APIs, webhooks, voice integrations, core banking connectors, databases, and observability platforms with direct customer engagement. You will also analyze AI agent behavior, identify recurring quality gaps, and turn technical findings into actionable improvements for Product and ML teams. Because the environment supports financial institutions, accuracy, compliance, documentation, and incident management are critical. This is a remote-first opportunity for a highly autonomous engineer who thrives on ownership, technical problem-solving, and continuous AI improvement.
Own escalated technical support cases end-to-end, including investigation, root-cause analysis, resolution, customer confirmation, and accurate case closure.
Troubleshoot complex issues across conversational AI flows, voice integrations, REST APIs, webhooks, core banking connectors, and other integrated systems.
Identify and categorize AI behavior issues such as hallucinations, intent misclassification, knowledge gaps, flow configuration problems, and integration failures.
Use logs, call recordings, digital transcripts, API traces, dashboards, and observability tools to diagnose production issues and identify underlying causes.
Analyze escalated AI conversations and provide sufficiently detailed root-cause insights for ML and Product teams to take corrective action.
Create, refine, and retire knowledge base content to improve AI resolution rates and continuously strengthen customer-facing experiences.
Communicate directly with customer technical and operational stakeholders through email and calls, providing clear updates and managing expectations throughout the resolution process.
Manage escalated tickets in Salesforce Service Cloud, ensuring timely acknowledgment, appropriate prioritization, resolution, and audit-ready documentation.
Lead P1 and P2 incident response activities, including war room coordination, timestamped updates, and parallel communication with Engineering and customer teams.
Prepare detailed post-mortems covering incident timelines, root causes, customer impact, resolutions, and preventive actions, including documentation suitable for leadership and compliance review.
File actionable bug reports that provide Engineering with sufficient information to investigate and resolve issues without unnecessary follow-up.
Identify recurring technical and AI behavior patterns and translate them into structured insights for Product and ML teams.
Escalate compliance-sensitive issues promptly, including concerns related to call recordings, disclosure language, and member data.
Maintain ownership of assigned queues, prioritize competing issues effectively, and independently drive cases to resolution.
Bring 5+ years of experience in technical support, implementation support, or a similar customer-facing technical role within a B2B SaaS environment.
Demonstrate strong hands-on experience debugging REST APIs and webhooks, including authentication failures, integration traces, and event-driven architectures.
Have practical experience with PostgreSQL or SQL for querying session data, analyzing usage patterns, and validating data integrity across integrated systems.
Be comfortable using Python for diagnostic scripts, log parsing, troubleshooting, and lightweight internal tooling.
Have experience with observability platforms such as OpenSearch, Kibana, Grafana, Datadog, or equivalent tools, including queries, dashboards, and distributed traces.
Understand voice technology concepts such as SIP and IVR routing and be able to distinguish ASR-related issues from AI model behavior.
Have experience using Salesforce Service Cloud or a comparable case-management platform for escalation tracking, documentation, and audit trails.
Familiarity with Kubernetes, including reading pod logs, understanding deployment states, and navigating cluster context during incident investigations, is an advantage.
Bring hands-on experience with AI agent creation and orchestration, including agent configuration, tool chaining, guardrails, and debugging multi-step workflows.
Be proficient with modern AI-assisted development and investigation tools such as Cursor and Claude Code, using them effectively for codebase exploration, troubleshooting, and scripting.
Be able to critically evaluate AI conversation transcripts across voice and digital channels and identify meaningful quality or behavioral issues.
Demonstrate excellent written and verbal communication skills, with the ability to work effectively with both technical and non-technical customer stakeholders.
Produce precise, audit-ready documentation that allows anyone reviewing a case to understand the situation, investigation, decisions, and outcome.
Demonstrate high agency, strong judgment, and a proactive approach to resolving issues without waiting for detailed direction.
Be highly self-directed and capable of independently managing a technical support queue in a fast-paced environment.
Demonstrate strong ownership, organization, problem-solving ability, and the resilience required to manage business-critical incidents.
Remote-first working environment with the flexibility and autonomy associated with a distributed team.
Opportunity to work at the forefront of generative AI and conversational AI within the financial services industry.
High-ownership role with significant autonomy over complex technical investigations and customer outcomes.
Exposure to advanced AI agent orchestration, APIs, integrations, observability, cloud technologies, and enterprise SaaS environments.
Opportunity to directly influence AI quality by turning production support cases and customer interactions into actionable improvements.
Collaboration with Engineering, Product, ML, and customer teams on technically challenging problems.
Comprehensive benefits package.
Opportunity to develop expertise in AI-powered customer service and enterprise financial technology.