New opportunity

AI Engineer

E2M Solutions · Ahmedabad, India

About this role

E2M Solutions seeks an AI Engineer for its Ahmedabad-based team. The role focuses on designing, building, and shipping AI-native applications that combine backend and frontend development. The engineer will deliver AI-driven products, custom dashboards, internal tools, and lightweight apps that address business problems.

Responsibilities

Develop end-to-end applications, including responsive frontends, REST APIs, and scalable Python backend services.
Integrate LLM APIs, including OpenAI, Claude, and Hugging Face, into applications. Implement prompt engineering, RAG pipelines, and context-aware AI systems.
Prototype and validate ideas quickly, then take features from concept to production. Optimize application performance, scalability, and cost, and collaborate with product and business teams to turn ideas into working solutions.

Required qualifications

At least 2 years of software or AI engineering experience, strong Python proficiency, and hands-on frontend development experience with React, Next.js, or similar tools.
Experience with LLM APIs and the ability to build and ship complete applications independently. Strong understanding of REST APIs, microservices, and system design; familiarity with prompt engineering, embeddings, and RAG; experience handling data with Pandas and NumPy; and strong problem-solving and debugging skills.
Comfort working across frontend and backend, rapidly iterating on prototypes, and applying AI in products rather than focusing solely on model training. The role calls for ownership from idea through production.

Preferred qualifications

Experience with LangChain, LlamaIndex, or similar frameworks; familiarity with vector databases such as Pinecone, Weaviate, or Supabase; experience building dashboards or internal tools; exposure to chat interfaces, copilots, and assistants; and familiarity with fast-prototyping tools and workflows.

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

Skills for this role

PythonReactNext.jsREST APIsMicroservicesSystem designOpenAI APIClaude APIHugging FacePrompt engineeringRAGEmbeddingsPandasNumPyLangChainLlamaIndexPineconeWeaviateSupabaseFrontend developmentBackend developmentRapid prototypingDebugging

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

Ship complete AI applications
The role calls for independent ownership from idea to production, spanning Python backend services, REST APIs, and responsive frontends built with React, Next.js, or similar tools.
Apply LLMs in working products
The engineer will integrate LLM APIs and implement prompt engineering, RAG pipelines, and context-aware systems. The posting emphasizes applying AI in products rather than focusing solely on model training.
Turn business needs into useful tools
Responsibilities include building AI-powered dashboards, internal tools, and lightweight apps, while collaborating with product and business teams to turn ideas into solutions.
Balance performance, scale, and cost
The role explicitly includes optimizing these qualities in AI-driven applications, alongside building scalable Python services.

Likely challenges to think through

Prototype quickly without losing production discipline
Preparation hypothesis: Fast iteration and quick validation may compete with the reliability needed to ship a feature. Prepare an example that distinguishes what you tested in a prototype from what you strengthened before release.
Make context-aware AI practical
Preparation hypothesis: RAG and LLM integration may require trade-offs among answer quality, latency, and cost. Prepare to explain how you would evaluate retrieval and responses, then adjust the design against those trade-offs.
Own both sides of the application
Preparation hypothesis: Building the frontend and backend independently may make interface design, API design, and debugging compete for time. Prepare an end-to-end example showing how you divided the work and traced a problem across the stack.

Practice your answers

Technical practice

How would you design an AI-native application from frontend to backend?

What to coverThe role involves shipping end-to-end applications with responsive frontends, REST APIs and Python services. Preparation suggestion: explain component boundaries, data flow and when a separate service would—or would not—help.

How would you structure a scalable Python REST API for an AI-powered tool?

What to coverThe posting calls for scalable Python backend services and an understanding of REST APIs, microservices and system design. Preparation suggestion: discuss endpoint design, error handling and how you would keep the service maintainable as usage grows.

How would you build a responsive frontend for a custom AI dashboard?

What to coverThe role includes React, Next.js or similar frontend work, as well as custom dashboards and internal tools. Preparation suggestion: describe how you would present results clearly, handle loading and error states, and connect the interface to backend APIs.

How would you integrate an LLM API into an application?

What to coverThe posting names OpenAI, Claude and Hugging Face as LLM API examples. Preparation suggestion: walk through request and response handling, failure cases and the trade-offs you would assess when choosing an integration.

How would you design and assess prompts for a context-aware feature?

What to coverPrompt engineering and context-aware AI systems are stated responsibilities. Preparation suggestion: explain how you would select relevant context and test whether responses are useful and consistent for the feature’s purpose.

How would you build a RAG pipeline, and when would you use one?

What to coverThe role calls for RAG pipelines and familiarity with embeddings; vector databases are listed as good to have. Preparation suggestion: cover retrieval, context assembly and answer assessment, including how you would investigate irrelevant retrieved content.

How would you use Pandas or NumPy to prepare data for an AI-powered dashboard?

What to coverThe posting requires experience handling data with Pandas and NumPy and includes AI-powered dashboards among the work. Preparation suggestion: describe cleaning and transforming data, checking results, and choosing what the dashboard should show.

How would you balance performance, scalability and cost in an AI-driven application?

What to coverOptimizing all three is an explicit responsibility. Preparation suggestion: identify what you would measure, compare potential changes against response quality, and explain how you would validate an improvement.

How would you move a lightweight AI tool from prototype to production?

What to coverThe role emphasizes fast iteration, quick validation and taking features from idea to production. Preparation suggestion: distinguish what you would test early from what you would strengthen before release, and explain how feedback would change the plan.

How would you debug an AI feature that gives poor results or fails intermittently?

What to coverStrong debugging skills and work across frontend, backend and LLM integrations are part of the posting. Preparation suggestion: describe how you would isolate the failing layer, reproduce the issue and verify that a fix addresses it.

Behavioural practice

Tell me about a feature you owned from idea through production.

What to coverThe posting emphasizes independent end-to-end delivery and ownership. Preparation suggestion: use a real example with the situation, your actions across the work, and the result; distinguish your contribution from the team’s.

Describe a time you turned a product or business need into a working solution.

What to coverCollaboration with product and business teams is a stated responsibility. Preparation suggestion: explain the situation, how you clarified the need and handled trade-offs, and what resulted.

Tell me about a prototype you changed after quick validation.

What to coverThe role calls for rapid prototyping and iteration. Preparation suggestion: describe the initial situation, what you built and learned, the action you took next, and the result.

Describe a difficult problem you debugged across application components.

What to coverThe posting requires strong problem-solving and debugging in a full-stack role. Preparation suggestion: give the situation, the steps you took to isolate and resolve the issue, and the outcome, without implying the problem was necessarily AI-specific.

Sharpen your knowledge

Topics to prepare

REST API and microservice design
The posting requires a strong understanding of REST APIs, microservices, and system design. Suggested exercise: sketch the frontend, Python service, and LLM API boundaries for an AI-powered internal tool; explain request flow and failure handling.
Embeddings and RAG
The posting calls for familiarity with embeddings and RAG and responsibility for building RAG pipelines. Suggested exercise: explain how documents become retrievable context, then identify where irrelevant retrieval could affect an answer.
Prompting and context-aware responses
Prompt engineering and context-aware AI systems are stated responsibilities. Suggested exercise: draft a prompt that uses retrieved context, then explain how you would revise it after an answer ignores that context.
Performance, scalability, and cost
The role explicitly includes optimizing all three for AI-driven applications. Suggested exercise: compare two designs for repeated requests and explain the trade-offs among response time, backend load, and LLM API usage.
Rapid prototyping through production
The posting emphasizes quick validation, collaboration with product and business teams, and ownership from idea to production. Suggested exercise: outline the decisions and checks you would make between an initial micro-app prototype and a releasable feature.
Optional retrieval frameworks and vector databases
LangChain or LlamaIndex and vector databases such as Pinecone, Weaviate, or Supabase are good-to-have, not required. Suggested exercise: explain what role a framework or vector database could play in the RAG design above, and when a simpler design might suffice.

Skills to revise

Build a Python REST service
Strong Python backend skills and scalable REST services are explicit requirements. Suggested exercise: implement a small endpoint for an AI-powered internal tool, including input validation and clear responses for failed requests.
Build a responsive frontend
The posting requires hands-on frontend development with React, Next.js, or similar tools. Suggested exercise: build a responsive interface that submits a request to your Python endpoint and displays loading, success, and error states.
Integrate an LLM API
Experience with LLM APIs such as OpenAI, Claude, or Hugging Face is required. Suggested exercise: connect one named API to the small application, construct its prompt from user input and context, and handle an unsuccessful API response.
Handle data with Pandas and NumPy
The posting requires experience with both for data handling. Suggested exercise: clean a small sample dataset with Pandas, calculate a summary using NumPy, and present the result in the internal-tool interface.
Debug and iterate on an end-to-end feature
Problem-solving, debugging, fast iteration, and shipping complete applications are emphasized. Suggested exercise: introduce a broken frontend-to-backend request, trace it to its cause, fix it, and describe how you would validate the change before release.

Your preparation checklist

Prepare an end-to-end application walkthrough
The posting emphasizes independently shipping AI-native applications. Prepare one project walkthrough, if available, covering the business problem, Python backend, REST API, responsive frontend, LLM integration, and path from prototype to production. Label any hypothetical example as such.
Sketch a dashboard or micro-app design
The role includes AI-powered dashboards, internal tools, and lightweight apps. Draft a small architecture diagram showing the user flow, React or Next.js interface, Python services, API boundaries, and how the design could scale.
Explain an LLM and RAG implementation
The posting calls for LLM APIs, prompt engineering, embeddings, RAG, and context-aware systems. Prepare a concise design showing retrieval and prompting steps, how responses would be checked, and where a vector database or framework might help; those tools are listed as preferred, not required.
Show evidence of rapid iteration and debugging
The posting expects quick validation and ownership through production. Prepare a short example, if available, with the initial prototype, feedback received, a bug investigated, the change made, and the resulting outcome.
Prepare a performance, cost, and data-handling discussion
The role calls for optimizing AI applications and using Pandas and NumPy. Outline how you would investigate slow or costly API requests and demonstrate a small data-cleaning or analysis example relevant to application output.
Draft questions about delivery expectations
Ask which dashboards, internal tools, or AI products are the immediate priorities; how product and business teams validate prototypes; and what performance, cost, and production-ownership expectations apply to this role.

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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