New opportunity

Full stack Gen AI Engineer

Biotale · Mumbai, India

About this role

Biotale seeks an experienced full-stack generative AI engineer to design, develop and implement generative AI products and applications, integrating them into existing systems. The full-time role is based in Mumbai, India, with a hybrid arrangement requiring two days in the office.

Responsibilities

Work across user interfaces, server-side logic, AI/ML frameworks, cloud technologies and automation tools. Manage the engineering lifecycle from research and development through deployment and optimization.
Guide development teams on architectural standards; conduct code reviews, tune performance and troubleshoot complex issues. Ensure solutions meet industry standards and security best practices.
Collaborate with cross-functional teams to define goals, develop solutions and support deployment. Contribute to proofs of concept and pilot projects.

Requirements

10+ years of hands-on software development experience and a minimum of 2 years in generative AI engineering.
Proficiency in Python and deep learning frameworks such as TensorFlow or PyTorch; experience designing model architectures; and knowledge of computer vision, image processing, real-time data processing and streaming technologies.
Front-end expertise with React or Flutter and responsive design principles; back-end expertise with Node.js and Python, including RESTful APIs, microservices architecture and integration.
Proficiency in DevOps practices, CI/CD pipelines and containerization; understanding of data integration; and a strong background in automation technologies.
Strong analytical, problem-solving and communication skills, including the ability to explain technical concepts to non-technical stakeholders and collaborate in a fast-paced, agile environment.

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

Skills for this role

Generative AIPythonTensorFlowPyTorchDeep learningModel architecture designComputer visionImage processingReal-time data processingStreaming technologiesReactFlutterResponsive designNode.jsRESTful APIsMicroservicesDevOpsCI/CDContainerizationData integrationCloud computingAutomationCode reviewPerformance tuningTechnical leadershipSecurity best practicesProblem-solvingCommunicationAgile collaboration

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

Deliver generative AI applications end to end
The role owns design, development and implementation of generative AI products, from research and development through deployment and optimization. Prepare an example that explains the decisions and outcomes at each stage.
Integrate the full application stack
The posting calls for user interfaces, Node.js or Python back ends, REST APIs, microservices and data integration with existing systems. Prepare to explain how you would connect an AI capability to a usable application.
Make sound AI engineering choices
The role specifies Python, deep learning frameworks and model architecture design, alongside knowledge of computer vision, image processing and streaming. Prepare to discuss when a model or processing approach fits a product need, rather than reciting tools.
Provide technical leadership
Responsibilities include architectural guidance, code reviews, performance tuning, complex troubleshooting and adherence to security best practices. Prepare examples of engineering decisions and how you communicated them to a team.
Work across teams from pilot to deployment
The engineer collaborates on goals and deployment and contributes to proofs of concept and pilots. Prepare to explain how you define a pilot's purpose and convey technical trade-offs to non-technical stakeholders.

Likely challenges to think through

Balancing pilot speed with production discipline
A likely tension is validating an idea quickly while preserving a path to secure, maintainable deployment; this is a preparation hypothesis, not a reported Biotale problem. Prepare to describe which safeguards you would establish early and what you would defer until validation.
Adding AI to existing systems
Integration may require trading off a clean new design against existing API, data and deployment constraints. Prepare a decision framework for service boundaries, data flow and incremental rollout; the posting does not specify the current architecture.
Managing performance across model and application layers
Optimization could involve competing demands from model behavior, API responsiveness and the real-time or streaming technologies named in the posting. Prepare to explain how you would measure bottlenecks before choosing a remedy; no particular latency target is stated.

Practice your answers

Technical practice

How would you design a generative AI application for integration into an existing system?

What to coverThe role covers generative AI application design, model architecture, Python, and TensorFlow or PyTorch. Preparation suggestion: Explain architecture choices, integration boundaries, and how you would evaluate quality against performance and operational constraints.

How would you approach a generative AI feature involving images?

What to coverThe posting calls for knowledge of computer vision and image processing. Preparation suggestion: Discuss preprocessing, model inputs and outputs, evaluation, and trade-offs such as image quality versus processing cost.

How would you handle real-time data in an AI application?

What to coverThe posting names real-time data processing and streaming technologies. Preparation suggestion: Describe how you would manage throughput, latency, failures, and data consistency without assuming a particular streaming tool.

How would you build a responsive interface for a generative AI application?

What to coverThe role calls for React or Flutter expertise and responsive design principles. Preparation suggestion: Explain how interface state, long-running requests, error feedback, and different screen sizes would shape your design.

How would you structure the back end for an AI application?

What to coverThe posting specifies Node.js and Python back-end development, RESTful APIs, microservices, and integration. Preparation suggestion: Discuss service boundaries, API contracts, failure handling, and when a simpler design may be preferable to additional services.

How would you integrate data from existing systems into an AI product?

What to coverThe role includes data integration and integrating generative AI applications into existing systems. Preparation suggestion: Cover data contracts, validation, access controls, and how you would diagnose an integration failure.

How would you take an AI application from development to deployment?

What to coverThe posting covers cloud technologies, automation, DevOps, CI/CD, containerization, and the full engineering lifecycle. Preparation suggestion: Outline a deployment path with testing, repeatable builds, release safeguards, and optimization, without assuming a specific cloud platform.

How would you investigate a performance regression across the application stack?

What to coverThe role includes performance tuning and troubleshooting complex issues from user interfaces through server-side logic and AI components. Preparation suggestion: Explain how you would isolate the bottleneck using measurements, then weigh fixes against reliability and maintainability.

How would you review the security of a generative AI application?

What to coverThe posting assigns responsibility for security best practices and compliance with industry standards. Preparation suggestion: Discuss risks at API, data, model-integration, and deployment boundaries, while avoiding claims about a specific mandated standard.

How would you decide whether a generative AI proof of concept should advance to a pilot?

What to coverThe role includes proofs of concept and pilot projects to validate new ideas. Preparation suggestion: Propose measurable goals, representative tests, and criteria for comparing quality, feasibility, integration effort, and operational cost.

Behavioural practice

When have you guided a team toward a sound technical architecture?

What to coverThe role includes technical guidance, architectural standards, and code reviews. Preparation suggestion: Use a real situation, your actions in explaining or reviewing the design, and the result; include how you handled differing technical views.

When have you aligned cross-functional teams on a project's goals?

What to coverThe posting calls for collaboration to define goals, develop solutions, and support deployment. Preparation suggestion: Give a real situation, the actions you took to resolve differing priorities, and the resulting decision or delivery outcome.

When have you explained a complex technical trade-off to non-technical stakeholders?

What to coverThe role explicitly calls for communicating technical concepts to non-technical stakeholders. Preparation suggestion: Describe the situation, how you framed the options and consequences, and the result of that conversation.

When have you resolved a difficult issue in a fast-paced team setting?

What to coverThe posting emphasizes analytical problem-solving and collaboration in an agile environment. Preparation suggestion: Use a real situation, your investigation and coordination actions, and the result, including what you changed afterward if relevant.

When have you helped a team test an uncertain idea before committing to it?

What to coverThe role includes contributing to proofs of concept and pilots that validate new ideas. Preparation suggestion: Describe the situation, how you agreed on a useful test with others, and the result, including a decision not to proceed if that was the outcome.

Sharpen your knowledge

Topics to prepare

Generative AI model architecture
The posting calls for generative AI engineering, Python, TensorFlow or PyTorch, and model architecture design. Suggested revision: sketch a model architecture, explain its design trade-offs, and describe how you would evaluate and optimize it.
Computer vision and image processing
The role explicitly asks for knowledge of both. Suggested revision: compare image preprocessing approaches and explain how their choices could affect a vision-based AI application.
Real-time processing and data integration
The posting names streaming technologies, real-time data processing, and data integration. Suggested revision: diagram how incoming data would be processed, passed to an AI application, and handled when processing falls behind.
Full-stack integration architecture
The role spans responsive React or Flutter interfaces, Node.js and Python back ends, RESTful APIs, and microservices integration. Suggested revision: draw the request and response flow for an AI feature integrated into an existing system, noting API boundaries.
Deployment, automation, and security
The posting covers CI/CD, containerization, automation, deployment, optimization, and security best practices. Suggested revision: outline a deployment path for an AI application and identify where automated checks and security reviews belong.
Technical leadership and proof-of-concept decisions
The role includes architectural guidance, code reviews, cross-functional collaboration, and proofs of concept. Suggested revision: prepare a short explanation of how you would scope a proof of concept, judge its results, and communicate trade-offs to non-technical stakeholders.

Skills to revise

Prototype a model in Python
Suggested exercise: implement a small generative AI prototype in Python using TensorFlow or PyTorch, then inspect an output and explain one architecture change. This practices the posting’s model-development requirements.
Connect a responsive interface to an API
Suggested exercise: build a small responsive React or Flutter screen that calls a RESTful endpoint written in Node.js or Python and displays its response. This rehearses the advertised front-end, back-end, and integration work.
Handle a streaming input
Suggested exercise: write a Python or Node.js component that processes a sequence of incoming records and records how it handles delayed or invalid data. This is preparation for the stated real-time processing and data-integration work.
Practice a deployment pipeline
Suggested exercise: containerize a small application and define CI/CD steps to check and deploy it. This directly revises the posting’s containerization, automation, and deployment responsibilities.
Review and troubleshoot code
Suggested exercise: review a sample AI application change for correctness, performance, API integration, and security concerns; then write concise feedback. This reflects the role’s code-review, performance-tuning, and troubleshooting duties.

Your preparation checklist

Map your experience to the stated requirements
Prepare a one-page timeline of relevant software development and generative AI engineering work, with specific evidence for the posting’s 10+ years and minimum 2 years requirements. Note which listed technologies you can discuss firsthand.
Build a generative AI project walkthrough
Choose a project you can discuss in depth. Prepare a concise account of the problem, model architecture choices, Python and TensorFlow or PyTorch work, integration into an application, results and trade-offs. Include computer vision or image processing only if relevant to that project.
Practice a full-stack integration design
Sketch how a generative AI application could connect a responsive React or Flutter interface to Node.js or Python services, REST APIs and existing systems. Explain data flow, service boundaries and how you would handle deployment and security; treat this as an interview exercise, not a description of Biotale’s architecture.
Prepare a real-time processing example
If you have relevant work, document one example involving streaming or real-time data: architecture, data integration, latency or reliability constraints, and how you measured outcomes. The posting names these areas but does not specify a particular platform.
Gather technical leadership evidence
Prepare brief examples of architectural guidance, code review, performance tuning and complex troubleshooting. For each, identify your decision, how you worked with the team and the observable result.
Rehearse a stakeholder discussion and questions
Practice explaining a technical trade-off to a non-technical stakeholder. Bring questions about the existing systems to integrate with, how PoCs and pilots are evaluated, cross-functional ownership and the role’s hybrid working expectations.

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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