1. Tell us about yourself
Answer
“I am an IT professional with 14+ years of experience in cloud architecture, data engineering, application modernization, and enterprise solutions. My recent focus has been on AWS cloud-native architectures, AI/ML platforms, Generative AI, and MLOps.
I have experience designing scalable systems using AWS services including EKS, Lambda, S3, DynamoDB, IAM, VPC, and CI/CD pipelines. I have worked on enterprise-grade solutions involving security, governance, compliance, and automation.
Recently, I have been focusing on Generative AI solutions using foundation models, RAG architectures, vector databases, prompt engineering, and Amazon Bedrock. My goal is to build secure and scalable AI platforms that align with business and regulatory requirements.”
2. What is Amazon Bedrock?
Answer
Amazon Bedrock is a fully managed AWS service that provides access to foundation models from multiple providers through a unified API.
Benefits:
- No infrastructure management
- Serverless architecture
- Supports multiple LLM providers
- Enterprise security
- Integration with AWS ecosystem
Supported model providers include:
- Anthropic (Claude)
- Meta (Llama)
- Amazon (Titan)
- Cohere
3. Why would you use Bedrock instead of OpenAI APIs?
Answer
| Bedrock | OpenAI |
|---|---|
| AWS Native | External API |
| Private Networking | Public Endpoint |
| IAM Integration | API Keys |
| VPC Endpoint Support | Limited |
| Multi-Model Support | Single Vendor |
| Enterprise Governance | Requires Additional Controls |
For regulated industries like banking and finance, Bedrock is often preferred due to security and compliance capabilities.
4. Explain Retrieval Augmented Generation (RAG)
Answer
RAG combines:
- User Query
- Retrieve Relevant Documents
- Send Context + Question to LLM
- Generate Response
Architecture:
User Query
→ Vector Search
→ Retrieve Documents
→ Bedrock LLM
→ Response
Benefits:
- Reduces hallucinations
- Uses private company data
- Improves accuracy
- No model retraining required
5. How would you implement RAG on AWS?
Answer
Components:
- S3 for documents
- Bedrock Titan Embeddings
- Vector Store
- OpenSearch
- Aurora PostgreSQL pgvector
- Lambda
- API Gateway
- Bedrock Claude
Flow:
Document → Embedding → Vector DB
User Query
→ Embedding
→ Similarity Search
→ Context Retrieval
→ Bedrock
→ Response
6. What is Amazon EKS?
Answer
Amazon EKS (Elastic Kubernetes Service) is a managed Kubernetes service.
Benefits:
- Managed Control Plane
- Auto Scaling
- High Availability
- IAM Integration
- Secure Workload Isolation
Used for:
- AI inference services
- Microservices
- MLOps platforms
- Containerized APIs
7. Why use EKS for AI workloads?
Answer
AI workloads often require:
- GPU resources
- Autoscaling
- High throughput
- Model serving
EKS provides:
- Horizontal Pod Autoscaler
- Node Autoscaling
- GPU scheduling
- Service mesh support
- Rolling deployments
8. Difference Between ECS and EKS?
Answer
| ECS | EKS |
|---|---|
| AWS Proprietary | Kubernetes |
| Easier | More Flexible |
| Less Control | Full K8s Control |
| Simple Apps | Enterprise AI Platforms |
For enterprise AI platforms, EKS is often preferred.
9. Explain Kubernetes Pods
Answer
A Pod is the smallest deployable unit in Kubernetes.
Contains:
- One or more containers
- Shared networking
- Shared storage
Example:
Python AI API
+
Inference Container
Running together inside a pod.
10. How do you deploy Python AI services on EKS?
Answer
Steps:
- Dockerize application
- Push image to ECR
- Create Deployment YAML
- Create Service
- Deploy using kubectl
Flow:
Python App
→ Docker
→ ECR
→ EKS Deployment
11. Explain AWS Lambda
Answer
Lambda is a serverless compute service.
Benefits:
- No server management
- Auto scaling
- Pay per execution
Common AI use cases:
- Triggering inference
- Data processing
- Event orchestration
- Document ingestion
12. What are Event-Driven Architectures?
Answer
Systems respond to events.
Example:
File Uploaded to S3
→ Event Notification
→ Lambda
→ Generate Embeddings
→ Store in Vector DB
Benefits:
- Loose coupling
- Scalability
- Cost efficiency
13. Explain API Gateway
Answer
API Gateway exposes REST or HTTP APIs.
Features:
- Authentication
- Throttling
- Monitoring
- Lambda integration
Flow:
Client
→ API Gateway
→ Lambda
→ Bedrock
→ Response
14. Explain IAM Best Practices
Answer
Follow Principle of Least Privilege.
Best practices:
- IAM Roles instead of Users
- Temporary Credentials
- MFA
- Resource-level permissions
- Role separation
Example:
Lambda Role:
Access only S3 bucket and Bedrock API.
15. What is a VPC?
Answer
Virtual Private Cloud provides isolated networking in AWS.
Components:
- Public Subnets
- Private Subnets
- Route Tables
- NAT Gateway
- Security Groups
Used for secure enterprise deployments.
16. What are VPC Endpoints?
Answer
VPC Endpoints allow private communication to AWS services without internet access.
Benefits:
- Enhanced Security
- Compliance
- Reduced Attack Surface
Common:
- S3 Endpoint
- Bedrock Endpoint
- DynamoDB Endpoint
17. Explain Security Groups vs NACLs
Answer
| Security Group | NACL |
|---|---|
| Instance Level | Subnet Level |
| Stateful | Stateless |
| Allow Rules Only | Allow & Deny |
18. Explain DynamoDB
Answer
DynamoDB is a fully managed NoSQL database.
Benefits:
- Millisecond latency
- Serverless
- Auto scaling
Use cases:
- Chat history
- Session storage
- User metadata
19. Why use S3 in AI Architectures?
Answer
S3 serves as:
- Data Lake
- Model Storage
- Training Data Repository
- Document Storage
Features:
- Versioning
- Encryption
- Lifecycle Policies
20. What CI/CD pipeline would you implement?
Answer
Tools:
- CodeCommit/GitHub
- CodeBuild
- CodePipeline
- ECR
- EKS
Flow:
Developer Commit
→ Build
→ Security Scan
→ Test
→ Deploy
→ Monitoring
21. How do you monitor AI applications?
Answer
Use:
- CloudWatch
- Prometheus
- Grafana
- X-Ray
Monitor:
- Latency
- Token Usage
- Error Rates
- Model Performance
- Infrastructure Health
22. Explain AI Governance
Answer
AI Governance includes:
- Model Approval Process
- Audit Logging
- Explainability
- Data Privacy
- Responsible AI Controls
Required heavily in financial institutions.
23. What compliance requirements exist in banking?
Answer
Common requirements:
- SOC2
- PCI DSS
- GDPR
- SOX
- FFIEC
- Data Encryption
- Audit Trails
- Access Controls
24. How would you secure a GenAI platform?
Answer
Layers:
- IAM Controls
- VPC Isolation
- Encryption
- Secrets Manager
- WAF
- Monitoring
- Audit Logging
Architecture:
User
→ WAF
→ API Gateway
→ Lambda/EKS
→ Bedrock
→ Private Data Sources
25. Scenario Question
Question
Design a secure enterprise GenAI chatbot for a bank.
Answer
Architecture:
- Frontend Application
- API Gateway
- Lambda/EKS
- Bedrock Claude
- OpenSearch Vector Database
- S3 Knowledge Repository
- IAM
- VPC Endpoints
- CloudWatch
- KMS Encryption
Security:
- No public data exposure
- Private networking
- Encryption at rest and transit
- Audit logs
- Role-based access
26. Explain Bedrock Guardrails
Answer
Bedrock Guardrails provide:
- Content filtering
- PII detection
- Toxicity prevention
- Prompt protection
- Response filtering
Critical for financial and regulated environments.
27. What is Prompt Engineering?
Answer
Prompt engineering is the practice of designing instructions that improve LLM outputs.
Techniques:
- Zero-shot
- One-shot
- Few-shot
- Chain of Thought
- Role-based prompting
28. What is an AI Agent?
Answer
AI Agents can:
- Reason
- Plan
- Call Tools
- Execute Actions
Example:
Customer asks account question
→ Agent retrieves account data
→ Uses Bedrock
→ Generates answer
29. Explain MLOps vs LLMOps
Answer
| MLOps | LLMOps |
|---|---|
| Traditional ML | Generative AI |
| Training Models | Prompt Management |
| Feature Engineering | Context Engineering |
| Model Monitoring | Hallucination Monitoring |
30. Most Important Interview Closing Question
Question
Why are you a good fit for this role?
Answer
“My background combines cloud architecture, AWS platform engineering, enterprise security, and modern AI technologies. I have extensive experience designing scalable and secure systems using AWS services such as EKS, Lambda, IAM, VPC, S3, and CI/CD pipelines. Additionally, I have been working with Generative AI, RAG architectures, prompt engineering, and foundation models. This combination of cloud expertise, AI knowledge, and enterprise governance aligns well with the requirements of building secure, scalable AI platforms in regulated financial environments.”
Focus Areas for This Interview (Highest Priority)
- Amazon Bedrock
- RAG Architecture
- AWS Lambda
- Amazon EKS
- IAM & Security
- VPC & Private Endpoints
- S3 + DynamoDB
- CI/CD for AI Platforms
- Kubernetes
- Financial Services Compliance
- GenAI Governance
- AI Agents & LLMOps
Mastering these topics should cover a large majority of the technical and architectural questions likely to arise from this job description.
