System Design & Cloud Architecture
As a leader in infrastructure, your ability to design resilient, scalable systems is critical. We evaluate your proficiency in designing cloud-native architectures, primarily focusing on AWS or GCP environments. Strong performance in this area means you can take an ambiguous prompt, define clear requirements, and design a system that balances performance, cost, and reliability.
Be ready to go over:
- Container Orchestration – Deep knowledge of Kubernetes, including scaling strategies, networking, and cluster management.
- Infrastructure as Code (IaC) – Advanced usage of Terraform or similar tools to manage complex, multi-region environments.
- Networking & Security – VPC design, IAM roles, load balancing, and securing sensitive financial data in transit and at rest.
- Advanced concepts (less common) – Multi-cloud failover strategies, service mesh implementations (like Istio), and custom Kubernetes operators.
Example questions or scenarios:
- "Design an infrastructure architecture to support a sudden 10x spike in traffic for our AI auditing endpoints."
- "How would you structure our Terraform modules to support multiple environments while minimizing code duplication?"
- "Walk me through how you would design a secure, highly available multi-region Kubernetes deployment."
AI Infrastructure & MLOps
Because Appzen relies heavily on machine learning, this specialized area evaluates your ability to support data science workflows. We look for candidates who understand the unique compute and storage requirements of AI models. A strong candidate will demonstrate experience in bridging the gap between traditional DevOps and MLOps.
Be ready to go over:
- Model Deployment Pipelines – CI/CD practices specifically tailored for machine learning models.
- Compute Provisioning – Managing GPU instances, auto-scaling based on queue depth, and optimizing cost for heavy workloads.
- Data Pipelines – Infrastructure supporting large-scale data ingestion, storage, and processing.
- Advanced concepts (less common) – Integrating tools like Kubeflow or MLflow, and optimizing GPU utilization through time-slicing or multi-instance GPUs.
Example questions or scenarios:
- "How would you design a pipeline to automatically test, validate, and deploy a new machine learning model to production?"
- "Our GPU costs are spiraling out of control. What strategies would you implement to optimize this infrastructure?"
- "Describe a time you had to troubleshoot a performance bottleneck in a heavy data-processing pipeline."
SRE Practices & Incident Management
Reliability is a core feature of our platform. This area tests your SRE mindset, focusing on how you measure, monitor, and maintain system health. We evaluate your approach to incident response and your ability to establish meaningful metrics. Strong candidates will speak fluently about SLIs, SLOs, and blameless post-mortems.
Be ready to go over:
- Observability & Monitoring – Implementing comprehensive logging, metrics, and tracing using tools like Datadog, Prometheus, or Grafana.
- Incident Response – Structuring on-call rotations, defining escalation policies, and managing critical outages.
- Capacity Planning – Forecasting resource needs based on business growth and historical data.
- Advanced concepts (less common) – Chaos engineering practices and automated remediation scripts.
Example questions or scenarios:
- "Walk me through your process for defining and implementing SLOs for a critical new microservice."
- "Tell me about the most severe production outage you managed. How did you lead the team through it, and what did you learn?"
- "How do you balance the need for feature velocity with the requirement to maintain strict reliability budgets?"
Leadership & Team Management
As the Manager, DevOps, SRE & AI Infrastructure, your technical skills must be matched by your ability to lead. We evaluate your experience in building teams, mentoring engineers, and driving cross-functional initiatives. Strong performance here involves providing concrete examples of how you have positively impacted team culture and output.
Be ready to go over:
- Team Building – Hiring strategies, onboarding processes, and fostering a diverse, inclusive team environment.
- Performance Management – Setting goals, conducting 1-on-1s, and handling underperformance constructively.
- Stakeholder Alignment – Negotiating priorities with product managers and engineering leaders.
- Advanced concepts (less common) – Managing remote or globally distributed infrastructure teams and leading through organizational restructuring.
Example questions or scenarios:
- "Describe a time you had to advocate for technical debt reduction over new feature development with non-technical stakeholders."
- "How do you measure the success and productivity of your SRE team?"
- "Tell me about an engineer you mentored who went on to achieve significant success. What was your approach?"
