Full Stack Engineer – Generative AI & Agentic AI

What matters most

• Own end-to-end application delivery

  The role covers frontend, backend, APIs, databases and cloud environments from design through support. Prepare to explain architectural decisions and how components are tested and maintained together.

• Integrate LLMs and agents into applications

  The posting calls for LLM applications, RAG, intelligent agents and workflow automation. Prepare a concrete design walkthrough covering retrieval, agent boundaries and how output quality would be evaluated.

• Apply AI across the development lifecycle

  Accenture describes using generative and agentic AI in requirements, design, coding, testing, deployment and operations, including coding copilots. Prepare to distinguish useful acceleration from work that still needs engineering review.

• Build reliable cloud-native delivery

  Scalable APIs and microservices must meet performance, reliability and security requirements; automated tests, CI/CD, IaC and DevSecOps support delivery. Prepare to explain the trade-offs behind a deployment and testing approach.

• Operate systems with cross-functional teams

  The role includes AI-driven observability and AIOps, plus collaboration with architects, product owners, AI specialists and business stakeholders. Prepare to describe how monitoring evidence and stakeholder needs inform engineering decisions.

Likely challenges to think through

• Balance AI-assisted speed with quality

  Preparation hypothesis: Faster coding or workflow automation may create more output to verify. Be ready to explain review, testing and evaluation checks before AI-generated changes or responses are trusted.

• Keep RAG and agent behavior dependable

  Preparation hypothesis: Retrieval quality and variable model behavior can complicate reliability and security. Prepare to discuss grounding, access boundaries, failure handling and evaluation without assuming a particular platform is mandated.

• Meet production demands across the stack

  Preparation hypothesis: Scaling cloud-native services while controlling performance and operational complexity requires trade-offs. Prepare an example architecture and explain what you would measure, test and adjust after deployment.

Topics to prepare

• Full-stack application architecture

  The posting covers frontend, backend, APIs, databases and cloud environments. For revision, sketch an end-to-end application and explain component boundaries, data flow, and trade-offs for scalability, maintainability and security.

• REST APIs and microservices

  Strong experience with RESTful APIs, microservices and database-driven applications is required. For revision, explain how you would define API contracts, handle failures between services and preserve data consistency.

• LLMs, prompting and RAG

  Understanding of LLMs, prompt engineering and RAG is required, and building LLM and RAG applications is part of the role. For revision, trace a question from retrieval through prompt construction to response, noting how you would check relevance and answer quality.

• Agentic workflows and AI safeguards

  The role includes intelligent agents and workflow automation; agent orchestration platforms, model evaluation and responsible AI are preferred experience. As preparation rather than an additional requirement, diagram an agent workflow with decision points, failure handling and evaluation checks.

• Cloud delivery and operations

  The posting calls for cloud-native applications, CI/CD, Infrastructure as Code, DevSecOps and AI-driven observability. For revision, walk through a deployment from code change to monitoring, identifying security, reliability and rollback considerations.

Skills to revise

• Build a full-stack feature

  The posting seeks proven full-stack development experience. Practise implementing a small database-backed feature with a frontend and backend, using technologies named in the posting that you already know, such as React and Node.js or Angular and .NET. Explain your design and tests.

• Implement and test a REST endpoint

  The role requires scalable APIs and automated testing. Practise adding a database-backed REST endpoint, then write tests for valid requests, invalid input and a downstream failure; explain the performance and security choices.

• Prototype a RAG flow

  The role includes building RAG architectures. Practise assembling a small retrieval-and-response flow using embeddings and a vector database, which the posting lists as preferred familiarity; inspect retrieved passages and evaluate whether the response is supported by them.

• Package and deploy a service

  The posting calls for hands-on Docker and Kubernetes experience and knowledge of cloud platforms and IaC. Practise containerizing a service, defining its Kubernetes deployment and outlining how Terraform or Bicep would provision the supporting infrastructure.

• Automate delivery and diagnose failures

  CI/CD, DevSecOps and application-health monitoring appear in the responsibilities. Practise describing a pipeline that runs tests and security checks before deployment, then use a simulated failure to explain what you would monitor and how you would investigate it.

• Prepare an end-to-end project walkthrough

  Select a real example spanning frontend, backend, REST APIs, databases and cloud deployment. Bring an architecture diagram and explain your contributions, design decisions, testing and outcomes; the posting calls for end-to-end application delivery.

• Practice a scalable service design

  Sketch a hypothetical microservice and API, including data flow, Docker/Kubernetes deployment, and performance, reliability and security trade-offs. This is a preparation exercise based on the role’s cloud-native responsibilities, not a stated interview task.

• Design an LLM feature on paper

  Outline a RAG application or agent workflow with retrieval, embeddings, prompts, orchestration, evaluation and failure handling. Distinguish core LLM/RAG understanding from production agent frameworks, which the posting lists as preferred.

• Gather delivery and operations evidence

  Prepare concise examples of automated tests, CI/CD, Infrastructure as Code and DevSecOps work, plus how application health was monitored and incidents addressed. If relevant, explain where AI-driven observability or AIOps would fit.

• Explain responsible AI-assisted development

  Prepare a specific account of how a coding copilot or other AI tool could support requirements, design, coding or testing while preserving code review, validation, security and quality controls. Label any approach you have not used as proposed rather than completed work.

• Prepare a cross-functional collaboration example

  Choose a real instance of working with technical and business stakeholders. Be ready to explain how requirements were clarified, trade-offs communicated and a delivery decision reached; the role names architects, product owners, AI specialists and business stakeholders.

• Write targeted questions for Accenture

  Bring questions about the team’s application architecture, where RAG or agents are used, how AI outputs are evaluated and governed, and expectations for delivery and operational support. These topics follow the posting; the answers are not specified there.