Role Purpose: Birlasoft is seeking a skilled AI Support Engineer to join their team to maintain, support, and optimize AI models and cloud infrastructure. Key Responsibilities: Provide technical support, troubleshooting, and optimization for generative AI models running on Azure AI services and AWS Bedrock. Monitor and optimize AI model performance to ensure high availability and reliability. Collaborate with development teams to integrate AI services with existing microservices built using Spring Boot. Utilize monitoring tools like Splunk and New Relic to track system health, diagnose issues, and improve system resilience. Assist in the deployment, scaling, and maintenance of AI models in cloud environments (Azure and AWS). Document support procedures, incident reports, and system configurations. Stay updated with the latest trends in AI, cloud services, and monitoring tools. Required Qualifications: Strong experience with Generative AI models (e.g., large language models, diffusion models, transformer architecture). Hands-on experience working with Azure AI services including deployment and monitoring. Familiarity with AWS Bedrock and its capabilities for managing and deploying foundation models in production. Mandatory skills include AI and ML (PL2), Large Language Models (PL2), Open AI (PL2), and Bedrock (PL2). Optional Qualifications: Experience with Stability AI (PL1). The role also requires proficiency in software design, configuration, quality assurance, and stakeholder management.
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Skills for this role
Generative AILarge Language ModelsAzure AI servicesAWS BedrockSpring BootSplunkNew RelicCloud InfrastructureTransformer architectureDiffusion modelsOpenAI
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
AI Model Optimization and Reliability
The core of the role involves maintaining and optimizing generative AI models on Azure AI and AWS Bedrock. You must prioritize ensuring high availability and performance, which requires deep technical knowledge of transformer architectures and the ability to troubleshoot production-level model issues.
Cross-Platform Cloud Integration
You are expected to bridge the gap between AI services and existing microservices built with Spring Boot. This requires proficiency in integrating cloud-based AI models into broader software architectures while maintaining system resilience and health using tools like Splunk and New Relic.
Operational Documentation and Knowledge Management
The role explicitly requires documenting support procedures, incident reports, and system configurations. You must prioritize clear, structured communication to ensure that AI deployments are maintainable and that knowledge is effectively shared across the development team.
Likely challenges to think through
Managing Hybrid Cloud AI Environments
Simultaneously supporting Azure AI services and AWS Bedrock presents a challenge in maintaining consistent operational standards. Prepare by reviewing the specific deployment and monitoring nuances of both platforms to ensure you can diagnose issues effectively regardless of the underlying infrastructure.
Balancing Model Performance with Microservices Stability
Integrating generative AI models into Spring Boot microservices can introduce latency or reliability bottlenecks. Prepare by understanding how to design for failure in distributed systems and how to use monitoring tools to isolate whether performance degradation originates from the model inference or the application layer.
03
Practice your answers
Technical practice
Generative AI Model Optimization
What to coverExplain your approach to monitoring and optimizing the performance of large language models in a production environment. Focus on metrics like latency, throughput, and cost, and how you would address performance degradation.
Azure AI vs. AWS Bedrock Integration
What to coverCompare the deployment workflows for generative AI models on Azure AI services versus AWS Bedrock. Discuss the trade-offs in terms of model management, scalability, and integration capabilities.
Microservices and AI Integration
What to coverDescribe how you would integrate an AI service into an existing Spring Boot microservices architecture. Address concerns regarding API security, asynchronous processing, and error handling between the service and the model.
System Health Monitoring with Splunk and New Relic
What to coverHow do you leverage Splunk and New Relic to diagnose issues in a cloud-based AI infrastructure? Explain your process for setting up alerts and tracking system resilience during model inference spikes.
Transformer Architecture Fundamentals
What to coverExplain the role of the attention mechanism in transformer architectures and how it influences the performance of large language models. Suggestion: Be prepared to discuss how model architecture choices impact deployment requirements.
Cloud Deployment and Scaling
What to coverWhat strategies do you use for scaling AI models in cloud environments to ensure high availability? Discuss the use of load balancing, auto-scaling groups, and containerization in the context of AI workloads.
Incident Management and Documentation
What to coverDescribe your process for documenting AI model incidents and system configurations. Explain why maintaining detailed logs and procedures is critical for long-term system reliability and knowledge management.
Behavioural practice
Stakeholder Relationship Management
What to coverDescribe a situation where you had to explain a complex technical AI issue to a non-technical stakeholder. How did you ensure they understood the impact and the proposed resolution?
Collaborating Across Teams
What to coverGive an example of a time you collaborated with a development team to integrate a new service. How did you handle conflicting priorities or technical disagreements to ensure a successful deployment?
Continuous Learning and Future Readiness
What to coverThe AI field evolves rapidly; describe a recent trend or technology you learned about and how you applied that knowledge to your work. Suggestion: Focus on how you stay updated with cloud-native AI advancements.
Results-Oriented Problem Solving
What to coverTell me about a time you identified a bottleneck in a system and took initiative to improve its resilience. What was the situation, what actions did you take, and what was the measurable result?
02
Sharpen your knowledge
Topics to prepare
Generative AI Architecture and Transformer Models
Review the core mechanics of transformer architecture, including attention mechanisms and encoder-decoder structures. Exercise: Explain how transformer-based models handle tokenization and context windows, as this is essential for troubleshooting performance issues in production LLMs.
Cloud-Native AI Model Lifecycle Management
Understand the end-to-end lifecycle of deploying models on Azure AI and AWS Bedrock. Exercise: Map out the steps for deploying a foundation model from a model registry to a production endpoint, focusing on scaling strategies and monitoring metrics relevant to high availability.
Microservices Integration with Spring Boot
Study the integration patterns for connecting AI service APIs with Java-based microservices. Exercise: Design a conceptual workflow where a Spring Boot application consumes an LLM endpoint, focusing on error handling, latency management, and secure credential configuration.
Skills to revise
Monitoring and Observability with Splunk and New Relic
Gain proficiency in setting up dashboards and alerts for cloud infrastructure. Exercise: Create a mock dashboard in a local environment to track API response times and error rates for a sample service, simulating how you would diagnose system health issues.
Azure AI and AWS Bedrock Configuration
Practice the configuration of managed AI services. Exercise: Review the documentation for Azure AI Studio and AWS Bedrock to identify the specific parameters for model deployment, fine-tuning, and monitoring that ensure production-grade reliability.
04
Your preparation checklist
Map AI Model Deployment Workflows
Prepare a technical walkthrough detailing your experience deploying Generative AI models on Azure AI Services and AWS Bedrock. Focus on how you managed model scaling, availability, and production-level monitoring.
Demonstrate Spring Boot Integration
Create a brief architectural diagram or code snippet showing how you have integrated AI-driven services into a Spring Boot microservices environment, highlighting how you handled API communication and error handling.
Prepare Monitoring and Incident Response Case Studies
Draft two examples of how you used Splunk or New Relic to diagnose system health issues in a cloud environment. Explain the specific metrics you tracked and the steps you took to improve system resilience.
Develop Documentation Samples
Prepare a template for an incident report or a system configuration guide. This demonstrates your ability to meet the role's requirement for documenting support procedures and maintaining technical knowledge bases.
Formulate Strategic Questions for the Interviewer
Prepare questions regarding the current AI model lifecycle at Birlasoft, such as how the team balances model performance optimization with cost management in cloud environments, or how they prioritize new AI service integrations.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
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