Gen AI Architect

What matters most

• Cloud architecture and deployment

  The role leads scalable, high-performance GenAI solutions on Azure and/or AWS, including Docker applications orchestrated with Kubernetes. It also calls for cloud-native model deployment using SageMaker, Azure AI, or Vertex AI.

• Agentic AI and RAG design

  The architect designs multi-agent systems and retrieval-augmented generation architectures, and sets standards for prompts, vector search, and model orchestration.

• MLOps and LLM observability

  The posting assigns ownership of pipelines spanning training, fine-tuning, evaluation, deployment, and monitoring, alongside LLM performance, system-health, and usage analytics.

• Enterprise integration and safeguards

  The role integrates AI into enterprise products with cross-functional teams, leads performance tuning and troubleshooting, and establishes secure, scalable, maintainable architecture practices.

• Hands-on technical leadership

  The architect develops models using Python or R and named ML frameworks, mentors AI engineers, and is expected to bring substantial technical architecture experience.

Likely challenges to think through

• Balancing agent autonomy with control

  Preparation hypothesis: Multi-agent workflows may add coordination and failure modes compared with simpler RAG designs. Prepare to explain when each architecture is justified and how you would evaluate output quality and operational behavior.

• Reconciling performance with production safeguards

  Preparation hypothesis: High-performance cloud deployment may compete with security, governance, and maintainability. Prepare an architecture example that explains those trade-offs and how you would test and troubleshoot them.

• Making model behavior observable across its lifecycle

  Preparation hypothesis: Connecting evaluation and MLOps pipelines to live LLM performance, health, and usage signals may be difficult. Prepare to describe which signals would trigger investigation, tuning, or a deployment decision.

Topics to prepare

• Cloud architecture, security and governance

  Revise how an Azure and/or AWS design addresses performance, scalability, security and maintainability. Exercise: sketch a client-facing GenAI architecture and explain its capacity, access-control and governance decisions. The posting explicitly requires cloud architecture expertise and enterprise AI security, scalability and governance.

• RAG, vector search and prompt standards

  Revise retrieval flow, vector-search relevance and prompt-design trade-offs. Exercise: draw a RAG request path and explain how you would assess retrieval quality versus response quality. The role designs RAG architectures and sets vector-search and prompt-engineering standards.

• Multi-agent orchestration

  Revise how agents coordinate tasks, pass context and handle failures. Exercise: diagram a multi-agent workflow and identify where orchestration should stop or retry. The posting explicitly calls for multi-agent systems and orchestration experience.

• MLOps and LLM observability

  Revise the links between training, fine-tuning, evaluation, deployment and monitoring. Exercise: map those stages to performance, system-health and usage measures, then explain how a regression would be investigated. All of these lifecycle stages and observability concerns appear in the posting.

• Enterprise integration and microservices

  Revise service boundaries and how AI capabilities can be integrated into enterprise products. Exercise: sketch where an AI service would meet a Spring Boot microservice and explain failure handling and maintainability. The posting requires Spring Boot and microservices experience and calls for cross-functional integration.

Skills to revise

• Containerize and orchestrate an AI application

  Practice packaging a small AI application with Docker and describing its Kubernetes deployment, scaling and troubleshooting steps. This directly reflects the required hands-on Docker and Kubernetes experience.

• Implement and evaluate a model

  Practice a small model-development exercise in Python or R with TensorFlow, PyTorch or scikit-learn; explain the evaluation result and a limitation. These are the languages and frameworks named in the posting.

• Prototype a retrieval workflow

  Practice connecting document retrieval, vector search and a prompt in a small RAG example; inspect a poor retrieval result and revise the flow. This is preparation for the advertised RAG, vector-search and prompt-engineering work, not a specified employer assignment.

• Trace a multi-agent failure

  Practice stepping through a sample agent handoff, recording its inputs, outputs and failure point, then proposing a recovery path. This is a suggested exercise for the posting’s multi-agent design and orchestration responsibilities.

• Plan cloud deployment and monitoring

  Practice outlining deployment of a model through AWS SageMaker, Azure AI or Google Vertex AI, including evaluation, monitoring and a response to degraded performance. The posting names these services and requires cloud-based model deployment, MLOps and LLM observability.

• Explain an AI service integration

  Practice sketching an interface between an AI capability and a Spring Boot microservice, then walk through an error and its diagnosis. This is a suggested hands-on preparation exercise tied to the required Spring Boot experience and enterprise integration work.

• Map the stated requirements to evidence

  Prepare a one-page record of relevant architecture decisions and hands-on work. The posting lists 6–15 years overall and separately requires 8+ years in a technical architecture role; keep the two statements distinct rather than assuming one replaces the other.

• Prepare an end-to-end project walkthrough

  If you have a relevant example, outline the problem, your decisions, architecture diagram, deployment, measurable results and trade-offs. Focus on scalable GenAI delivery on Azure and/or AWS, including Docker and Kubernetes where applicable.

• Practice a RAG and multi-agent design exercise

  Sketch a hypothetical enterprise use case with retrieval, vector search, prompts and agent orchestration. Be ready to explain evaluation, failure handling, security boundaries and why each component is needed; this is preparation, not a stated interview exercise.

• Document a model-to-production workflow

  Prepare an example or design covering Python or R model development, a relevant listed ML framework, cloud deployment, and MLOps stages from training or fine-tuning through evaluation, release and monitoring. Distinguish tools you have used from those you would propose.

• Rehearse an operational troubleshooting scenario

  Walk through how you would investigate an AI application's performance or quality issue using LLM performance, system-health and usage signals. Explain the optimization choices and how you would validate the fix.

• Show enterprise integration and technical leadership

  Prepare a concise example, if available, of integrating AI with microservices or Spring Boot, working across teams, setting security and governance practices, and mentoring engineers. State your own contribution and the resulting outcome.

• Write targeted questions for the employer

  Ask which client use cases and cloud platforms matter most, how agentic AI and RAG are evaluated and governed, and what architecture ownership the role carries. Clarify the posting's Chennai listing versus Pan India role location, hybrid arrangement, in-person interview mode and immediate-to-90-day notice-period statement.