New opportunity

Full Stack Engineer, AI Platform

Accrete · Mumbai, India

About this role

Accrete seeks a Full Stack Engineer for its AI Platform team in Mumbai, India. The team builds core infrastructure, tools and user-facing applications for developing, deploying and scaling AI systems. The role spans frontend and backend engineering, connecting AI capabilities with practical user needs. Accrete’s AI agents analyze data, generate insights and make recommendations for businesses and government organizations.

Responsibilities

Design and deliver end-to-end features, scalable APIs and services for AI-driven applications, and intuitive interfaces for interacting with models and data.
Work with ML engineers to integrate models into production systems. Improve platform reliability, performance and developer experience.
Contribute to architecture and technical direction, and own projects from concept through deployment and iteration.

Required qualifications

3–5 years of full-stack software engineering experience; strong experience with modern frontend frameworks such as React and TypeScript, and backend development in Python, Go or similar languages.
Experience building and consuming REST or GraphQL APIs; familiarity with AWS, GCP or Azure; and experience with SQL and/or NoSQL databases.
A product mindset, the ability to turn requirements into technical solutions, and comfort working in a fast-paced, evolving environment. The role calls for ownership, cross-team collaboration, problem-solving and enthusiasm for AI innovation.

Preferred qualifications

Experience with AI/ML systems or data-intensive applications; Docker and Kubernetes; real-time systems or streaming architectures; or security, compliance or government-focused environments.

The position is full-time with hybrid work and remote flexibility under company guidelines. Benefits listed include competitive salary aligned with experience and market standards, 10 Lacs of family mediclaim coverage, flexible paid time off, professional development support, onsite lunches and snacks, and company-sponsored social events.

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

Skills for this role

Full-stack developmentReactTypeScriptPythonGoRESTGraphQLAWSGCPAzureSQLNoSQLAPI developmentCloud infrastructureSoftware architectureAI/ML systemsDockerKubernetesReal-time systemsStreaming architecturesProduct thinkingCross-functional 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 end-to-end AI application features
The role builds features across frontend and backend systems, including interfaces for interacting with AI models and data. Its product focus is translating user requirements into technical solutions.
Build scalable APIs and integrate models
Develop services for AI-driven applications and work closely with ML engineers to bring models into production systems.
Improve platform quality and developer experience
The team builds infrastructure and tools for developing, deploying, and scaling AI systems; this role is explicitly responsible for improving reliability, performance, and developer experience.
Own delivery and shape technical direction
Contribute to architecture, carry projects from concept through deployment and iteration, and collaborate across teams. The posting emphasizes accountability, informed decisions, and clear communication.

Likely challenges to think through

Making complex AI useful in an interface
Preparation hypothesis: presenting model outputs intuitively while retaining enough context for users may require trade-offs between simplicity and detail. Prepare a concrete example of how you would turn an AI-related user need into an interface and refine it through feedback.
Coordinating the path from model to production
Preparation hypothesis: integrating work from ML engineers into scalable services may make interface contracts and deployment responsibilities difficult to align. Prepare to explain how you would agree on API behavior, handle integration changes, and collaborate on a release.
Balancing delivery speed with platform quality
Preparation hypothesis: owning projects in an evolving environment while improving reliability, performance, and developer experience may create competing priorities. Prepare an example of how you would justify an architecture choice, set a workable scope, and use evidence to decide what to improve after deployment.

Practice your answers

Technical practice

How would you turn a user need into an end-to-end AI platform feature?

What to coverThe role involves building features across frontend and backend systems and translating requirements into technical solutions. Suggested preparation: explain how you would clarify the user need, divide responsibilities across the stack, and use feedback after deployment to guide iteration.

How would you design an interface for interacting with AI models and data?

What to coverThe posting calls for intuitive user interfaces and names React and TypeScript as examples of relevant frontend experience. Suggested preparation: discuss how you would present model outputs clearly, handle loading and error states, and check whether users can act on the information.

When would you choose REST or GraphQL for an application API?

What to coverThe role calls for building and consuming REST or GraphQL APIs. Suggested preparation: compare client data needs, API evolution, operational complexity, and how you would make errors understandable to consumers.

How would you design a scalable backend service for an AI-driven application?

What to coverThe posting calls for scalable APIs and services and backend experience in Python, Go, or a similar language. Suggested preparation: explain how you would define service boundaries, manage growing demand, and validate performance without assuming a particular stack.

How would you integrate an ML model into a production application?

What to coverThe role involves collaborating with ML engineers to integrate models into production systems. Suggested preparation: cover the interface between the service and model, how you would handle unavailable or slow responses, and how both teams would verify the integration.

How would you choose between SQL and NoSQL storage for platform data?

What to coverThe posting asks for experience with SQL and/or NoSQL databases. Suggested preparation: frame the choice around data relationships, query patterns, consistency needs, and how the choice affects application development.

How would you plan deployment of a platform service in the cloud?

What to coverThe posting asks for cloud infrastructure familiarity; Docker and Kubernetes are listed as nice to have. Suggested preparation: describe the deployment considerations you would evaluate, including configuration, scaling, and rollback, while treating container orchestration as one possible approach rather than a stated requirement.

How would you investigate a platform slowdown or reliability problem?

What to coverImproving reliability, performance, and developer experience is an advertised responsibility. Suggested preparation: explain how you would isolate the affected layer, use evidence to prioritize a fix, and confirm the change improves both service behavior and maintainability.

What trade-offs arise if an AI application needs real-time or streaming updates?

What to coverReal-time systems and streaming architectures appear under nice-to-have experience. Suggested preparation: compare update timeliness with implementation complexity and reliability, and explain when a simpler request-based approach might suffice.

How would security or compliance concerns affect an AI platform feature?

What to coverExposure to security, compliance, or government-focused environments is listed as nice to have, and the company describes government users. Suggested preparation: discuss how you would identify data-handling and access questions early and work with relevant stakeholders, without assuming a specific regulatory framework.

Behavioural practice

Tell me about a project you owned from concept through iteration.

What to coverThe posting emphasizes end-to-end ownership and accountability. Suggested preparation: use a real situation, explain the actions you personally took through deployment and feedback, and give the result, including what you changed afterward.

Describe a time you collaborated across specialties to deliver a feature.

What to coverThe role specifically involves working with ML engineers and values shared goals across teams. Suggested preparation: give the situation, your actions to establish interfaces or resolve dependencies, and the result for the joint work.

Tell me about a time requirements changed while you were building something.

What to coverThe posting calls for a product mindset and comfort in a fast-paced, evolving environment. Suggested preparation: describe the situation, how you clarified priorities and adapted your technical plan, and the resulting outcome.

Describe a technical disagreement you resolved constructively.

What to coverThe stated collaboration expectations include listening, candid communication, and addressing conflicts constructively. Suggested preparation: explain the situation, how you incorporated others' perspectives and reached a decision, and what happened afterward.

Tell me about a new AI-related idea you evaluated for practical impact.

What to coverThe posting values curiosity about AI alongside meaningful user outcomes. Suggested preparation: use a real situation, explain how you learned about and evaluated the idea, and state the result—even if you decided not to adopt it.

Sharpen your knowledge

Topics to prepare

End-to-end feature architecture
The role contributes to architecture and owns features from concept through iteration. Practice suggestion: sketch the frontend, API, service and data boundaries for an AI-insights feature; explain the tradeoffs and how you would change the design after feedback.
REST and GraphQL API design
Building and consuming REST or GraphQL APIs is required. Practice suggestion: design an endpoint or query for retrieving generated insights, then reason through response shape, errors and scalability.
SQL and NoSQL data modeling
The posting requires experience with SQL and/or NoSQL databases. Practice suggestion: model stored insights and their source-data references, and explain which access patterns would guide your database choice.
Production model integration
The job involves working with ML engineers to integrate models into production. Practice suggestion: diagram how an application would request a model-generated recommendation and handle an unavailable or slow response. This is a preparation scenario, not a specified Accrete design.
Cloud reliability and performance
Cloud familiarity is required, and improving platform reliability and performance is a responsibility. Practice suggestion: review a hypothetical slow AI-backed request and identify where you would measure latency, investigate failures and make a deployment tradeoff.
Product requirements and cross-team ownership
The posting emphasizes translating requirements into technical solutions, collaboration and ownership. Practice suggestion: turn a request for a clearer insights interface into a scoped plan, noting questions for product and ML colleagues and what you would revisit after release.

Skills to revise

Build a React and TypeScript interface
Strong frontend experience, with React and TypeScript as examples, is requested. Practice suggestion: build a small view that displays insights and recommendations from sample API responses, including loading, empty and error states.
Implement a backend service
Backend experience in Python, Go or a similar language is required. Practice suggestion: implement a small service that accepts an insight request, validates it and returns a structured response; use a stubbed model response rather than assuming a specific model stack.
Connect frontend and backend APIs
The role builds end-to-end features and consumes APIs. Practice suggestion: connect the interface to your service through REST or GraphQL, then test how the UI responds to successful and failed requests.
Persist and retrieve application data
Database experience is required. Practice suggestion: save and retrieve insight records using a SQL or NoSQL database you know, and test a query that supports the interface's main view.
Trace and improve a deployed feature
The role covers deployment and iteration and calls for better reliability, performance and developer experience. Practice suggestion: walk through deploying your small feature on a cloud platform you know, then diagnose a simulated slow or failing request. Docker and Kubernetes are listed only as nice-to-have.

Your preparation checklist

Map the stated requirements
Create a one-page evidence table for full-stack experience, React or TypeScript, Python or Go (or similar), REST or GraphQL APIs, cloud infrastructure, and SQL or NoSQL. Note which examples you can substantiate; the posting asks for 3–5 years of full-stack experience.
Prepare an end-to-end project walkthrough
Choose a real project, if available, and outline the user need, frontend and backend decisions, your ownership from concept through deployment, and what changed after feedback. These stages reflect the role’s stated responsibilities.
Practice an AI-feature design exercise
As preparation, sketch an interface for interacting with model outputs and data, plus an API and database design behind it. Explain trade-offs, failure states, and how the feature serves a user need; this is an exercise, not a stated interview assignment.
Outline a production model integration
Prepare a diagram or spoken walkthrough showing how you would work with ML engineers to connect a model to an application. Mark assumptions and decisions about the interface, deployment, and handling unreliable responses; model integration is a stated responsibility.
Document platform improvements
Bring one concrete example, if available, of improving reliability, performance, or developer experience. Record the problem, your contribution, evidence of the result, and the architectural trade-off, since the role calls for improvement and technical direction.
Prepare collaboration and ownership examples
Draft two brief accounts of turning evolving requirements into a technical solution and resolving a cross-team challenge. Identify your decisions and collaborators’ input; the posting emphasizes product thinking, accountability, and candid collaboration.
Write targeted questions for Accrete
Ask how the AI Platform team divides work between full-stack and ML engineers, what reliability or developer-experience problems matter most, and how it evaluates user-facing AI features. If relevant, ask whether the listed preferred areas—such as Kubernetes, streaming, or security—matter for current projects.

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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