New opportunity

AI Engineer (5–8 Years)

Aligned Automation Services Pvt Ltd · Pune, India

About this role

Aligned Automation seeks an AI Engineer in Pune, India to design, develop and deploy generative and agentic AI solutions for enterprise-scale applications. The role focuses on turning business use cases into production-ready AI systems.

Responsibilities

Build multi-agent workflows using frameworks such as LangGraph, LangChain or CrewAI, and develop and optimize RAG systems using vector databases and enterprise knowledge sources.
Create scalable Python APIs and microservices with FastAPI; implement LLM orchestration, prompt engineering, evaluation and observability.
Integrate AI solutions with enterprise applications, workflows and data platforms. Work with business stakeholders and follow enterprise software development lifecycle, security, governance and deployment standards.

Requirements

The role specifies 5–8 years of experience, while the job information section lists 3–8 years.
Strong Python programming and hands-on experience with generative AI, LLMs and agentic AI frameworks.
Experience with RAG, vector databases, embeddings and semantic search; knowledge of LangChain, LangGraph, CrewAI, AutoGen, MCP or similar frameworks.
Experience building REST APIs with FastAPI or Flask, plus an understanding of system design, software engineering and cloud-native architectures.
Familiarity with AI evaluation, monitoring and observability, and strong problem-solving and communication skills.

This is a full-time position in Pune, India.

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

Skills for this role

PythonGenerative AIAgentic AILarge Language ModelsLangGraphLangChainCrewAIAutoGenMCPRAGVector DatabasesEmbeddingsSemantic SearchFastAPIFlaskREST APIsMicroservicesLLM OrchestrationPrompt EngineeringAI EvaluationAI MonitoringAI ObservabilitySystem DesignCloud-native ArchitectureProblem-solvingCommunication

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 agentic workflows
The role calls for multi-agent workflows using LangGraph, LangChain, CrewAI, or similar frameworks. Prepare to explain how you would structure orchestration and decide when multiple agents are warranted.
Develop enterprise RAG systems
The posting emphasizes optimizing RAG with vector databases and enterprise knowledge sources. Prepare to discuss retrieval, embeddings, semantic search, and how you would assess answer quality.
Deliver scalable AI services
Python APIs and microservices, particularly with FastAPI, are explicit duties, alongside integration with enterprise applications, workflows, and data platforms. Prepare to explain service boundaries and integration choices.
Make LLM behavior measurable
The role includes prompt engineering, evaluation frameworks, monitoring, and observability. Prepare an example of how you would measure quality and use results to improve an AI system.
Translate use cases into governed deployments
The engineer works with business stakeholders to turn use cases into production-ready systems while following enterprise SDLC, security, governance, and deployment standards. Prepare to explain how requirements and controls shape design decisions.

Likely challenges to think through

Balancing agent autonomy and predictability
Inferred preparation challenge: a multi-agent design may handle complex tasks but add coordination and failure modes. Prepare to explain when a simpler workflow is preferable and how you would evaluate the chosen design.
Balancing retrieval quality and operational cost
Inferred preparation challenge: searching enterprise knowledge broadly may improve coverage while increasing latency or irrelevant context. Prepare to describe how you would test retrieval choices against answer quality and service performance.
Moving from use case to compliant production service
Inferred preparation challenge: rapid iteration on an AI use case can conflict with enterprise security, governance, and deployment requirements. Prepare to explain how you would surface those constraints early with stakeholders rather than address them only at release.

Practice your answers

Technical practice

How would you design a multi-agent workflow for an enterprise use case?

What to coverThe role includes building multi-agent workflows with frameworks such as LangGraph, LangChain, or CrewAI. Suggested answer focus: explain how you would divide responsibilities, coordinate agents, handle failures, and decide when a simpler workflow would suffice.

How would you build a RAG system using enterprise knowledge sources?

What to coverThe posting calls for RAG, embeddings, vector databases, and semantic search. Suggested answer focus: walk through preparing source content, indexing it, retrieving relevant material, and grounding generated answers.

How would you troubleshoot a RAG system that returns irrelevant answers?

What to coverThe role explicitly includes optimizing RAG systems. Suggested answer focus: distinguish retrieval failures from generation failures, describe what you would inspect, and explain how you would test whether a change helped.

How would you structure prompts and LLM orchestration for a production application?

What to coverThe posting names prompt engineering and LLM orchestration as responsibilities. Suggested answer focus: discuss prompt structure, component boundaries, handling unexpected outputs, and trade-offs between flexibility and predictable behavior.

How would you expose an AI capability through a Python REST API?

What to coverThe role calls for scalable Python APIs and microservices using FastAPI, and lists FastAPI or Flask experience. Suggested answer focus: describe request and response design, validation, error handling, and how the API would interact with the AI workflow.

How would you design an AI microservice to scale in a cloud-native architecture?

What to coverThe posting asks for system design, software engineering, and cloud-native architecture knowledge. Suggested answer focus: explain service boundaries, workload scaling, dependencies, and the trade-offs you would assess before deployment.

How would you evaluate an agentic AI solution before release?

What to coverThe role includes evaluation frameworks for LLM-based systems. Suggested answer focus: define checks tied to the use case, assess both answer quality and workflow behavior, and explain how results would inform release decisions.

How would you monitor and investigate a deployed AI workflow?

What to coverThe posting specifies AI monitoring and observability. Suggested answer focus: identify useful signals across requests, retrieval, and agent steps, then explain how you would trace a failure without exposing sensitive enterprise data.

How would you integrate an AI service with existing enterprise workflows and data platforms?

What to coverIntegrating with enterprise applications, workflows, and data platforms is a stated responsibility. Suggested answer focus: explain how you would establish data and service boundaries, manage dependencies, and validate that the integration meets the business use case.

How would security and governance affect deployment of an enterprise AI system?

What to coverThe role requires adherence to enterprise SDLC, security, governance, and deployment standards. Suggested answer focus: discuss access to knowledge sources, review and testing before release, and trade-offs between capability and controlled use.

Behavioural practice

Tell me about a time you turned an ambiguous business use case into a technical plan.

What to coverThe role involves working with business stakeholders to translate use cases into production-ready AI systems. Suggested answer focus: give the situation, how you clarified needs and agreed on scope, and the resulting decision or outcome.

Describe a time you collaborated across teams to integrate a new capability into an existing system.

What to coverThe posting emphasizes enterprise integration and collaboration. Suggested answer focus: describe the situation, your actions to coordinate interfaces or resolve differing priorities, and the result.

Tell me about a difficult technical problem you investigated systematically.

What to coverStrong problem-solving is listed as a required skill. Suggested answer focus: use a real example to explain the situation, the evidence you gathered and actions you took, and what the result showed.

Describe a time delivery pressure conflicted with a security or deployment standard.

What to coverThe role calls for adherence to enterprise security, governance, SDLC, and deployment standards. Suggested answer focus: explain the situation, how you addressed the trade-off with others, and the outcome without implying that controls were optional.

Tell me about a time you had to learn an unfamiliar technical approach to solve a problem.

What to coverThe posting lists several agentic AI frameworks and highlights curiosity as part of the company culture. Suggested answer focus: use a real situation to show how you evaluated the approach, applied what you learned, and assessed the result.

Sharpen your knowledge

Topics to prepare

Multi-agent orchestration
The role calls for multi-agent workflows and LLM orchestration. As a revision exercise, diagram an agent workflow with task handoffs, state, stopping conditions, and failure handling; explain when a simpler workflow would suffice.
RAG and semantic retrieval
The posting specifies RAG, embeddings, semantic search, vector databases, and enterprise knowledge sources. Compare chunking, retrieval, and grounding choices for a sample document collection, then explain how you would diagnose an irrelevant answer.
Enterprise AI system design
The role requires system design, cloud-native architecture, and integration with enterprise applications and data platforms. Sketch an API-to-retrieval-to-LLM architecture and explain scaling, service boundaries, and integration points.
AI evaluation and observability
Evaluation frameworks, monitoring, and observability are named in the posting. Define a small test set and explain what you would measure for answer quality, retrieval quality, failures, and operational behavior.
Production governance and stakeholder translation
The role involves translating business use cases into production-ready systems while following enterprise SDLC, security, governance, and deployment standards. Practice turning a hypothetical stakeholder request into acceptance criteria, a delivery plan, and questions about data access and review.

Skills to revise

Build a Python agent workflow
The posting requests strong Python skills and multi-agent workflows using frameworks such as LangGraph, LangChain, or CrewAI. Practice implementing a small workflow in one of those frameworks with explicit state transitions, error handling, and a trace of each handoff.
Implement a small RAG pipeline
Because the role develops and optimizes RAG systems, practice embedding a small document set, storing it in a vector database, retrieving relevant passages, and checking whether generated answers cite the retrieved material.
Expose an AI workflow through a REST API
The posting specifies scalable Python APIs and microservices using FastAPI and lists FastAPI or Flask for REST APIs. Practice building a FastAPI endpoint around a sample workflow, including input validation and clear error responses.
Test prompts and inspect execution
Prompt engineering, evaluation, and observability are explicit responsibilities. Practice revising a prompt against a fixed set of cases, recording outcomes and failure examples, and tracing the workflow step responsible for a poor result.
Explain an integration design
The role integrates AI with enterprise applications, workflows, and data platforms. As a preparation exercise, use a hypothetical application to outline request and data flows, interface contracts, and how the service would handle unavailable dependencies.

Your preparation checklist

Prepare an agent-workflow walkthrough
The role calls for multi-agent workflows and LLM orchestration. Prepare a diagram of a relevant project, if available, or a clearly labeled sample design showing agent responsibilities, tool use, failure handling, and why you chose a framework.
Map out a RAG system
The posting specifies enterprise knowledge sources, embeddings, vector databases, and semantic search. Prepare a retrieval pipeline diagram and explain how you would test retrieval quality and keep source content current.
Practice a production API design
Sketch a Python REST service using FastAPI or Flask around an AI workflow. Be ready to explain scalability, integration with enterprise applications and data platforms, and cloud-native deployment trade-offs.
Assemble evaluation and observability evidence
Prepare examples or a sample test plan covering prompt or workflow changes, evaluation criteria, monitoring, and how you would investigate poor responses in production.
Rehearse a stakeholder-to-delivery example
The role involves translating business use cases into production-ready systems under enterprise SDLC, security, governance, and deployment standards. Prepare a concise account of the decisions, communication, and constraints involved; distinguish actual experience from a hypothetical approach.
Write targeted employer questions
Ask which agent and RAG use cases are priorities, what enterprise integration and evaluation standards apply, and which frameworks the team uses. Clarify the experience range: the role states 5–8 years, while Job Information lists 3–8.

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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