New opportunity

AI Consultant

Cloud EZE Private Limited · Bangalore, India

About this role

The AI Consultant designs, builds and integrates AI-powered solutions across enterprise cloud platforms and business applications. The role spans backend development, API integration, LLM integration, prompt engineering and AI automation. The consultant works with experienced consultants and architects on client projects to improve enterprise systems and workflows.

Requirements

2–5 years of professional experience in software development, AI/ML engineering or technical consulting.
Strong programming skills in Python, JavaScript/TypeScript, Java or a similar language; hands-on experience with LLM APIs and frameworks such as OpenAI, Anthropic Claude or Hugging Face.
Proficiency in prompt engineering and an understanding of LLM optimization techniques, plus experience building and integrating APIs, databases and scalable backend systems.
Ability to translate business requirements into technical solutions, communicate technical concepts to non-technical stakeholders, and adapt in a dynamic consulting environment.

Preferred

Experience with Workday or other enterprise cloud platforms such as SAP, Oracle or Salesforce; knowledge of RAG systems and vector databases.
Exposure to AI/ML model deployment and MLOps; experience with AWS, GCP or Azure; understanding of enterprise integration patterns and API architectures; or consulting and client-facing project experience.

The full-time position is listed in Bangalore, India (also described as Bengaluru). The posting mentions a highly flexible work policy but does not specify a work mode.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonJavaScriptTypeScriptJavaLarge Language ModelsOpenAIAnthropic ClaudeHugging FacePrompt EngineeringLLM OptimizationAPI IntegrationDatabasesBackend DevelopmentProblem SolvingCommunicationWorkdaySAPOracleSalesforceRetrieval-Augmented GenerationVector DatabasesModel DeploymentMLOpsAWSGCPAzureEnterprise Integration

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Build end-to-end AI solutions
The role covers designing, building and integrating AI-powered solutions for enterprise cloud platforms and business applications, from backend development through AI automation.
Make LLM integrations effective
Hands-on LLM API integration, prompt engineering and understanding of LLM optimization are stated requirements. The posting also identifies RAG and vector databases as good-to-have knowledge.
Connect AI to enterprise systems
Building and integrating APIs, databases and scalable backends is required. Experience with Workday or other enterprise cloud platforms is preferred, not required.
Translate client needs into technical work
The consultant works on client projects with experienced consultants and architects. Translating business requirements into technical solutions and explaining technical concepts to non-technical stakeholders are explicit expectations.

Likely challenges to think through

Balancing AI behavior with integration constraints
Preparation hypothesis: An LLM feature may work in isolation but be harder to fit into an existing application and backend. Prepare to explain how you would make design trade-offs across prompting, API integration and scalability; the posting does not specify a particular client system.
Scoping ambiguous business requests
Preparation hypothesis: Client goals may need clarification before they can become technical requirements. Prepare an example of defining the workflow, success criteria and integration boundaries, then communicating those choices without jargon.
Adapting across enterprise platforms
Preparation hypothesis: Varied client systems may make reusable approaches compete with platform-specific needs. Prepare to discuss how you would learn an unfamiliar platform and coordinate with architects, without assuming Workday expertise is mandatory.

Practice your answers

Technical practice

How would you turn a client's workflow problem into an AI solution design?

What to coverThe role involves translating business requirements into technical solutions for enterprise systems. Suggested preparation: explain how you would clarify the workflow and success criteria, then decide which parts need an LLM, backend service, API integration or conventional automation.

How would you integrate an AI service with an enterprise application's API?

What to coverAPI integration and enterprise integration patterns are named in the posting. Suggested preparation: discuss authentication, data mapping, error handling and how you would handle changes to an external API.

How would you design a backend and database for a scalable AI-powered application?

What to coverThe posting calls for experience with databases and scalable backend systems. Suggested preparation: explain data storage and access choices, handling concurrent requests, and the trade-off between synchronous responses and background work.

How would you build an application feature using an LLM API or framework?

What to coverHands-on LLM API and framework experience is stated as a requirement. Suggested preparation: describe request construction, response handling, failure cases and how you would keep the integration maintainable without assuming a particular provider.

How would you develop and refine a prompt for a business workflow?

What to coverPrompt engineering is explicitly part of the role. Suggested preparation: explain how you would define the expected output, test representative inputs and revise the prompt when responses are inconsistent or unsuitable.

How would you evaluate and optimize an LLM-powered feature?

What to coverThe posting asks for an understanding of LLM optimization techniques. Suggested preparation: compare output quality, latency and cost against the workflow's needs, and explain how you would test whether a change improves results.

When would you use AI automation rather than a rules-based workflow?

What to coverAI automation and improving enterprise workflows are stated duties. Suggested preparation: contrast tasks suited to deterministic rules with those that may benefit from an LLM, including how you would manage uncertain outputs.

How would you design a retrieval-augmented generation feature?

What to coverKnowledge of RAG systems and vector databases is listed as good to have. Suggested preparation: outline document preparation, retrieval and response generation, and explain how you would check whether retrieved material supports an answer.

What would you consider when connecting AI to Workday or another enterprise cloud platform?

What to coverExperience with Workday, SAP, Oracle or Salesforce is listed as good to have; the posting does not prescribe a specific integration. Suggested preparation: discuss access permissions, data boundaries and platform API constraints before choosing an integration approach.

How would you deploy and maintain an AI feature in the cloud?

What to coverModel deployment, MLOps and experience with AWS, GCP or Azure are listed as good to have. Suggested preparation: explain deployment, monitoring and rollback considerations, distinguishing an application that calls an LLM API from one that hosts a model.

Behavioural practice

Tell me about a time you clarified an ambiguous business requirement.

What to coverThe role calls for translating business requirements into technical solutions. Use a real situation, the questions and actions you took to reach a workable scope, and the resulting decision or outcome.

Tell me about a time you explained a technical trade-off to a non-technical stakeholder.

What to coverCommunication with non-technical stakeholders is an explicit requirement. Describe the situation, how you adapted your explanation and what the stakeholder was able to decide or do as a result.

Tell me about a time you collaborated with specialists to deliver a technical solution.

What to coverThe posting describes working with experienced consultants and architects on client projects. Give a real example covering your contribution, how you resolved differing views or dependencies, and the result.

Tell me about a time you had to learn quickly as project needs changed.

What to coverThe posting emphasizes fast learning and adaptation in a dynamic consulting environment. Explain the situation, what you learned and changed in your approach, and the effect on the work.

Tell me about a time you found and resolved a quality issue before or after delivery.

What to coverThe posting emphasizes quality alongside building and integrating solutions. Suggested preparation: choose a real example and describe the issue, your investigation and actions, and the observable result without overstating your individual role.

Sharpen your knowledge

Topics to prepare

LLM integration and optimization
Required: Review how an application calls an LLM API, handles responses, and assesses output quality. Exercise: sketch an integration using one named option—OpenAI, Anthropic Claude, or Hugging Face—and explain two ways you would compare prompt results. Relevant to the stated LLM integration and optimization work.
Backend and API architecture
Required: Review how APIs, databases, and backend components fit together in a scalable application. Exercise: diagram a business application sending a request through a backend to an LLM API and storing the result; explain where failures would be handled. Relevant to the posting’s backend and integration requirements.
Business requirements to technical design
Required: Review how to turn a workflow problem into a solution with clear inputs, outputs, and boundaries. Exercise: take a hypothetical request to automate a business-application task and write three clarifying questions plus a brief proposed design. Relevant to client projects and communication with non-technical stakeholders.
Enterprise platform integration
Preferred, not required: Review how an AI feature might connect to an enterprise cloud platform such as Workday, SAP, Oracle, or Salesforce. Exercise: draw a conceptual data flow between a platform, an API, and an AI-powered application. Relevant to the enterprise systems in the role; the flow is a preparation example, not a stated project.
RAG and vector databases
Preferred, not required: Review retrieval-augmented generation and the role of a vector database. Exercise: explain, with a simple diagram, how retrieved material could inform an LLM response and how you would check whether retrieval helped. Relevant to the posting’s good-to-have knowledge.

Skills to revise

Implement an LLM API call
Required: In one language named in the posting, write a small function that sends an input to an LLM API and handles the response and an API error. Practice explaining its integration point in a backend application. Relevant to the hands-on programming and LLM API requirements.
Test and refine prompts
Required: Draft a prompt for extracting structured information from a sample business request. Test it against several varied inputs, record inconsistent outputs, and revise the prompt. Relevant to prompt engineering and LLM optimization.
Build an API-backed workflow
Required: Implement a small backend endpoint that validates a request, calls a service, and reads or writes a database record. Walk through how you would handle a failed call. Relevant to building and integrating APIs, databases, and backend systems.
Present a solution to a non-technical stakeholder
Required: Prepare a two-minute explanation of the hypothetical workflow solution, covering the business outcome, what the application would do, and one unresolved question. Relevant to translating requirements and communicating on client projects.

Your preparation checklist

Prepare a project walkthrough
Choose a project you actually worked on involving backend development, APIs or AI integration. Explain the business need, your contribution, architecture, trade-offs and measured outcome; the posting calls for building AI-powered enterprise solutions.
Map your evidence to the core requirements
Create a one-page list of specific examples covering your strongest programming language, LLM API or framework use, prompt engineering, databases and scalable backends. Mark any gaps rather than claiming experience you do not have.
Practice an LLM integration design
Sketch a hypothetical API-backed workflow for a business application, showing data flow, prompt design, error handling and how you would evaluate and optimize outputs. Present it as an exercise, not a company project.
Review relevant enterprise integrations
Prepare to discuss any experience you have with Workday, SAP, Oracle or Salesforce. If relevant, add a simple integration diagram; the posting lists these platforms and enterprise integration patterns as preferred, not required.
Rehearse a client-facing explanation
Turn one technical example into a brief explanation for a non-technical stakeholder: the business requirement, proposed solution, trade-offs and how you would confirm it works.
Draft questions for the employer
Ask which enterprise platforms and AI use cases the role currently emphasizes, how consultants and architects divide design work, and what the listed flexible work policy means for the Bengaluru position.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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