Senior Backend Engineer - SG

What matters most

• Building an End-to-End AI Evaluation Platform

  The core responsibility is designing a system for step-level evaluation, automated regression analysis, and release quality gates. You must ensure that evaluation results serve as a trusted, production-grade signal for release decisions, requiring high reliability and benchmarking against industry standards.

• Implementing Robust Data Governance and Security

  You are tasked with building a security layer that includes real-time PII masking, audit controls, and least-privilege access interfaces. This requires ensuring full auditability and compliance without direct database connections, which is critical given the company's focus on regulated data environments.

• Developing Reusable Platform Capabilities

  To scale engineering efficiency, you must build shared SDKs, common workflow definitions, and management APIs. The goal is to reduce integration friction, enabling new evaluation data sources to be onboarded within a single day.

• Integrating AI/LLM Techniques into Infrastructure

  You must identify opportunities to replace brittle, rule-based logic with ML/LLM approaches. This involves prototyping and validating these models to improve the platform's governance and evaluation capabilities.

Likely challenges to think through

• Balancing High-Velocity Development with Strict Compliance

  Plaud operates under multiple compliance frameworks (ISO, SOC 2, HIPAA, GDPR). The challenge is to maintain rapid development velocity while ensuring that every platform change adheres to strict data security and privacy standards.

• Designing for 'No Direct Database Access' Architectures

  The requirement to implement data governance without direct database connections necessitates complex abstraction layers. You will need to prepare by demonstrating how to build secure, performant access interfaces that maintain auditability while abstracting the underlying data storage.

• Managing Distributed System Reliability at Scale

  As the platform grows, maintaining SLA targets and observability across microservices becomes difficult. You should be prepared to discuss how you reason from first principles regarding failure modes, retries, and circuit breaking in distributed environments.

Topics to prepare

• Distributed Systems Reliability

  Review core concepts of high availability, idempotency, and eventual consistency. Exercise: Design a failure recovery path for a distributed microservice architecture, specifically focusing on how to maintain data integrity during network partitions or service outages. This is critical for the role's requirement to build robust, production-grade platform components.

• Data Governance and Security Patterns

  Study principles of least-privilege access, PII masking techniques, and auditability in regulated environments. Exercise: Draft a conceptual architecture for a data access layer that prevents direct database connections while ensuring full audit logs for AI agent interactions. This aligns with the company's focus on secure, compliant AI infrastructure.

• Automated Evaluation and Quality Gates

  Understand the lifecycle of automated regression testing and release quality gates. Exercise: Define a set of metrics and a workflow for a 'quality gate' that determines if a new model version is ready for production deployment. This is directly relevant to the role's responsibility for building an end-to-end evaluation platform.

Skills to revise

• Workflow Orchestration with Argo

  Practice defining complex, multi-step workflows using Argo Workflows. Exercise: Create a sample YAML workflow that triggers a data processing job, performs a validation check, and reports the status to an external API. This is a specific hands-on requirement for the role.

• Go or Java Microservices Development

  Refine skills in building production-ready microservices using Go or Java. Exercise: Implement a service that utilizes gRPC for communication and includes built-in resilience patterns like circuit breaking and rate limiting. This is essential for the platform engineering tasks described.

• Modern Data Infrastructure Integration

  Gain familiarity with querying and managing data in environments like Spark, Flink, or Snowflake. Exercise: Write a script to perform a schema synchronization or data extraction task from a data lake or warehouse environment. This addresses the requirement for experience with modern data processing frameworks.

• Review Distributed Systems Architecture

  Prepare to discuss your experience building production-grade microservices using Go or Java. Focus on your ability to implement fault isolation, circuit breaking, and rate limiting in high-scale environments. Be ready to explain how you handle idempotency and eventual consistency in distributed data systems.

• Prepare Data Governance and Security Case Studies

  Draft examples of how you have implemented PII masking, least-privilege access, and audit controls in a regulated environment. Be prepared to explain how you design systems that provide data access without direct database connections, ensuring full auditability.

• Develop an Evaluation Platform Walkthrough

  Create a high-level design for an automated evaluation and regression testing platform. Explain how you would structure step-level evaluation, define quality gates for releases, and use workflow orchestration tools like Argo Workflows to manage these processes.

• Formulate AI-Native Engineering Examples

  Identify specific instances where you replaced rule-based logic with ML or LLM techniques to improve system performance. Prepare to discuss how you utilize AI-assisted tools to accelerate coding, debugging, and incident analysis in your daily workflow.

• Prepare Strategic Questions for the Interviewer

  Develop questions regarding the current challenges of their evaluation platform, the specific data governance hurdles they face with AI agents, and how the team balances rapid feature delivery with the strict compliance requirements of ISO 27001 and HIPAA.