Production Support Specialist

Your tasks

This hands-on engineering position focuses on active defect resolution, deep-dive debugging, and production support. The candidate will investigate, reproduce, and resolve live application issues before escalating, providing diagnostic handoffs rather than raw symptoms. This role works alongside Site Reliability Engineers (SREs) to ensure immediate stability while SREs address long-term systemic fixes.

Requirements

You are a hands-on production engineering specialist who thrives on solving complex defects post-launch. Rather than passing off raw symptoms, you analyze code, trace stack outputs, and isolate root causes to resolve live issues. You bring strong technical diagnostic skills, calm communication, and a solve-first mindset to keep client applications stable and reliable.

Core Qualifications & Experience

• Background: Degree in Computer Science, IT, or a related field, accompanied by hands-on experience in L2/L3 support, application support, or run engineering for production systems.
• Code Debugging (Primary Skill): Proven ability to navigate, trace, and debug existing codebases—primarily in Python (with TypeScript as a strong asset). Skilled at tracing stack traces and vague user reports down to specific lines of code or configuration errors.
• Root Cause Analysis (RCA): Disciplined approach to forming, testing, and ruling out hypotheses to isolate true root causes from superficial symptoms.
• Observability & Logging: Fluent in reading logs and traces using tools like Datadog, Splunk, ELK, or CloudWatch to locate failing components from user-reported issues.
• Kubernetes Diagnostics: Practical kube fluency to inspect pods, logs, events, and restart patterns. (Cluster architecture/building experience is not required.)
• Scripting & Automation: Strong scripting skills in Python or Bash to automate tasks and actively reduce operational work volume.
• Communication & Stakeholder Management: Excellent written and verbal English skills to draft thorough RCAs for engineering teams and clear status updates for non-technical or senior investment stakeholders under high-pressure situations.
• Mindset & Approach: Self-driven, collaborative, and fast-learning troubleshooting approach with a "solve-first, escalate-second" mentality.

Preferred / Bonus Qualifications

• AI/LLM Operations: Experience supporting AI/LLM products, including managing non-deterministic outputs and logic-based edge cases.
• Advanced Monitoring: Experience creating and configuring Datadog dashboards and alerts.
• Cloud & Infrastructure: Foundational knowledge of AWS services and practical Kubernetes pod troubleshooting.
• Enterprise Workflows: Hands-on experience with SQL/NoSQL databases, enterprise directory access management (Active Directory/entitlements), deployment/release verification, and formal change control processes.