Support Engineer (AI/ML & Platform Operations)

What matters most

• AI Evaluation and Failure Mode Analysis

  The role requires hands-on inspection of LLM outputs, conversation logs, and execution traces to identify hallucinations and edge cases. You must be able to translate these technical findings into actionable improvements for prompts, system logic, and workflows.

• High-Severity Incident Management

  You will serve as the primary technical escalation point for customer IT leadership during P1 incidents. This requires balancing strict SLAs with the need to provide clear, non-technical explanations of complex AI and platform failures.

• Cloud and LLM Pipeline Debugging

  You must maintain platform reliability by debugging workflows hosted on AWS and GCP. This involves isolating issues across model hosting services, API gateways, and reasoning pipelines using telemetry tools like Grafana and Kibana.

• Cross-Functional Engineering Partnership

  You are expected to collaborate with data science and engineering teams to deliver permanent fixes. This includes documenting investigation traces in tools like Jira or ServiceNow and updating runbooks to prevent recurring issues.

Likely challenges to think through

• Interpreting Non-Deterministic AI Behavior

  Unlike traditional software, LLM-based agents can produce non-deterministic outputs. Prepare by practicing how to systematically isolate whether a failure stems from the prompt, the underlying model, or the tool-calling structure.

• Communicating Technical Complexity to Stakeholders

  Explaining why an autonomous agent failed to a non-technical customer leader is difficult. Prepare by developing a framework to translate technical RCA findings into business-impact summaries that maintain customer trust.

• Balancing Reactive Support with Proactive Optimization

  The role demands both immediate P1 incident resolution and long-term workflow optimization. Prepare to discuss how you prioritize urgent outages while ensuring you still have time to contribute to systemic improvements and data labeling.

Topics to prepare

• LLM Failure Modes and Evaluation

  Understand how to identify hallucinations, prompt injection, and tool-calling errors in autonomous agents. Exercise: Review a set of sample LLM conversation logs and categorize them into specific failure types like 'context window overflow', 'incorrect tool selection', or 'factual hallucination' to practice diagnostic labeling.

• Root Cause Analysis (RCA) for Cloud-Native AI

  Learn the methodology for tracing failures across distributed systems, specifically API gateways and model hosting services. Exercise: Create a hypothetical incident timeline for an AI agent failure, mapping the issue from the user-facing error back through the API gateway to the specific model inference log.

Skills to revise

• Cloud Observability and Telemetry

  Proficiency in querying and visualizing logs using tools like Grafana or Kibana. Exercise: Practice writing a KQL or PromQL query to isolate error rates for a specific service endpoint over a 24-hour period to simulate identifying a performance bottleneck.

• SQL for Data Integrity Validation

  Writing complex queries to verify backend data consistency. Exercise: Write a SQL query that joins two tables to identify orphaned records or data mismatches that could cause an AI agent to fail during an HR or Finance workflow execution.

• API Payload Debugging

  Inspecting and validating JSON/XML structures in REST/SOAP requests. Exercise: Take a malformed JSON API payload and identify the schema violation that would prevent an LLM agent from correctly parsing the input.

• Diagnostic Scripting (Python/Bash)

  Automating the collection of system logs or diagnostic data. Exercise: Write a simple Python script that parses a log file to extract and count occurrences of specific HTTP status codes, helping to automate the initial triage of a P1 incident.

• Prepare a structured RCA case study

  Document a past high-severity incident where you performed root-cause analysis on a complex SaaS platform issue. Clearly define the problem, the telemetry tools used (e.g., Datadog, Grafana), the diagnostic steps taken, and the final resolution or workaround provided to stakeholders.

• Develop an LLM troubleshooting framework

  Create a brief explanation of your process for debugging LLM pipelines. Focus on how you identify hallucinations, analyze conversation logs, and evaluate prompt performance. Be prepared to discuss how you translate these findings into actionable improvements for system logic.

• Review cloud and API diagnostic skills

  Refresh your knowledge on debugging cloud-hosted services on AWS or GCP. Practice interpreting JSON/XML API payloads and writing complex SQL queries for data integrity validation, as these are core requirements for diagnosing AI workflow failures.

• Prepare for stakeholder communication scenarios

  Draft a strategy for explaining a technical AI failure to non-technical IT leadership. Focus on maintaining transparency, managing expectations during P1 incidents, and clearly articulating the path to resolution.

• Formulate questions for the interviewers

  Prepare thoughtful questions regarding the Agent Factory initiative, such as how the support team collaborates with data science teams on model interpretability or how the team balances proactive platform optimization with reactive incident management.