New opportunity

AI Engineer

G2 · Bengaluru, India

About this role

G2 is hiring an on-site AI Engineer in Bengaluru to build and deliver AI-driven products and features for its software marketplace. The role spans the full software stack and involves working with engineers, designers and product stakeholders to turn feature concepts into production-ready user journeys.

Responsibilities

Design and implement observable, maintainable and scalable solutions, contributing from architecture through release.
Build features and agents using frontier models, prompting, context, tools and model reasoning to generate content, support user journeys and automate processes.
Validate functionality and performance through traditional QA, evaluations and tracing so quality is measurable and reproducible.
Use agentic software-engineering techniques throughout planning, design, implementation, testing and maintenance.
Ideate, explore and prototype with designers, product managers and engineers; help refine requirements, scope projects and set priorities.

Minimum qualifications:

3+ years of professional programming experience in web-application and backend environments.
Strong proficiency in TypeScript/JavaScript or Python; strong knowledge of a modern web framework such as React, Next.js or Rails and a backend service framework such as FastAPI or Node.js.
Hands-on experience building AI- and agent-powered features with frontier models from OpenAI, Anthropic or Google; regular use of coding-agent harnesses such as Claude Code, Codex, Opencode or Pi in daily development; and experience refining feature requirements with a product stakeholder.

Qualifications that can strengthen an application include producing AI-generated content for end users; multimodal AI and speech-to-text or text-to-speech models; real-time, full-duplex communication applications; speech-to-speech models; and developing evaluations for agents. The position is full-time and on-site in Bengaluru.

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

Skills for this role

TypeScriptJavaScriptPythonReactNext.jsRailsFastAPINode.jsAI agentsPrompt engineeringOpenAI modelsAnthropic modelsGoogle AI modelsClaude CodeCodexOpencodePiSoftware engineeringSystem designFull-stack developmentBackend developmentAI evaluationTracingQuality assurancePrototypingMultimodal AISpeech-to-textText-to-speechSpeech-to-speech modelsReal-time communication

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 AI features across the full stack
The role spans architecture through release for production AI-driven user journeys. Prepare to explain how you connect web and backend implementation with observable, maintainable, scalable system design.
Build useful model-powered features and agents
G2 calls for frontier models, prompting, context, tools and model reasoning to generate content, support user journeys and automate processes. Prepare to explain the design choices behind a model-powered feature, rather than only naming a model.
Make quality measurable
The posting explicitly pairs traditional QA with evaluations and tracing to validate functional requirements and make results reproducible. Prepare to describe what each method checks and how its findings inform changes.
Shape deliverable product scope
The engineer will prototype with designers, product managers and engineers, refine requirements, and help scope and prioritize projects. Prepare an example of turning an AI feature concept into a bounded, testable deliverable.
Use coding agents throughout development
Regular use of coding-agent harnesses is a minimum qualification, and the role applies agentic techniques from planning through maintenance. Prepare to explain how agent-assisted work is reviewed and tested within a normal engineering workflow.

Likely challenges to think through

Flexible model behavior versus reproducible quality
A likely preparation challenge, not a stated G2 problem: agent outputs can vary while the role requires measurable, reproducible quality. Prepare an approach that combines functional checks, representative evaluations and traces to investigate failures.
Rapid prototyping versus production design
A likely trade-off is exploring feature concepts quickly without carrying a fragile prototype into a scalable user journey. Prepare to explain when you would narrow scope, revise architecture and add observability before release.
Agent-assisted speed versus engineering ownership
Because the role uses coding agents throughout the software lifecycle, a likely difficulty is gaining speed without losing understanding of changes. Prepare to describe review, testing and maintenance practices for agent-produced work.

Practice your answers

Technical practice

How would you design a full-stack AI-driven user journey from architecture through release?

What to coverThe role spans the full stack and calls for production-ready AI features. Preparation suggestion: explain how the user interface, backend and model interaction fit together, then discuss release readiness and the trade-off between a quick prototype and a maintainable implementation.

How would you implement the boundary between a web application and an AI-backed service?

What to coverThe posting names web frameworks such as React, Next.js or Rails and backend frameworks such as FastAPI or Node.js. Preparation suggestion: use a framework you know to explain request handling, validation, error reporting and how the interface responds when a model call fails.

How would you choose prompts and context for a model-powered feature?

What to coverThe role explicitly involves prompting, context and frontier models. Preparation suggestion: describe how you would select relevant context, test whether it improves the user journey and balance answer quality against latency and cost.

When should an AI agent use a tool rather than respond directly?

What to coverThe posting calls for agents that use tools and model reasoning to generate content or automate processes. Preparation suggestion: explain tool boundaries, how you would check outputs and what the agent should do when a tool fails or returns an unsuitable result.

How would you evaluate an agent-powered feature against functional requirements?

What to coverThe role calls for traditional QA alongside evaluations that make quality measurable and reproducible. Preparation suggestion: distinguish deterministic tests from agent evaluations, describe representative cases and explain how you would investigate a regression.

How would tracing help you diagnose a poor AI response?

What to coverTracing and observability are explicit responsibilities. Preparation suggestion: explain which steps of a model or tool interaction you would inspect, how you would locate the failure and how you would verify that a change improved the outcome.

What trade-offs would you make to keep an AI feature scalable and maintainable?

What to coverThe posting calls for observable, maintainable, scalable solutions. Preparation suggestion: discuss service boundaries, failure handling and operational signals, explaining which complexity you would accept now and which you would defer.

How would you use a coding-agent harness across the software development lifecycle?

What to coverRegular use of coding-agent harnesses is a minimum qualification, and the role applies agentic techniques from planning through maintenance. Preparation suggestion: explain where an agent can help with implementation or tests, how you review its work and when you would not rely on it.

How would you assess AI-generated content before showing it to end users?

What to coverGenerating content for users is part of the role; demonstrated experience doing so is listed as an additional strength. Preparation suggestion: discuss checks for relevance, accuracy and consistency with the intended user journey, including how to handle an unsatisfactory output.

Optional area: How would you assess an audio-based, real-time AI experience?

What to coverMultimodal and audio models, full-duplex applications and speech-to-speech models are listed as ways to strengthen an application, not minimum requirements. Preparation suggestion: if this area matches your experience, discuss latency, turn-taking and how you would evaluate the resulting user experience.

Behavioural practice

Tell me about a time you refined a feature concept with a product stakeholder.

What to coverCollaborative requirements refinement is specified in the posting. Preparation suggestion: use a real situation, explain the questions and trade-offs you raised, and state what changed as a result.

Tell me about a time you prototyped an idea with designers or other engineers.

What to coverThe role involves ideation, exploration and prototyping with cross-functional partners. Preparation suggestion: describe the situation, what you built or tested, how you incorporated feedback and the resulting decision.

Tell me about a time you helped scope or prioritize engineering work.

What to coverPartnering with engineers on project scoping and prioritization is a stated duty. Preparation suggestion: explain the competing needs, your contribution to the decision and the outcome using a real example.

Tell me about a time testing or production evidence changed your approach to a feature.

What to coverThe role emphasizes release quality, QA, evaluations and tracing. Preparation suggestion: give a real situation, identify the evidence you acted on, describe your corrective action and explain the result.

Tell me about a time you supported a teammate through a difficult technical decision.

What to coverG2 describes collaboration, learning from mistakes and kindness as part of its values. Preparation suggestion: choose a real situation, explain how you handled disagreement or uncertainty, and describe the outcome for the work and the team.

Sharpen your knowledge

Topics to prepare

AI feature and agent design
Posting: Features and agents use frontier models, prompting, context, tools and model reasoning. Suggested revision exercise: diagram a hypothetical user journey and explain what context the model needs, when it should use a tool, and how the result reaches the user.
Production system design
Posting: Full-stack solutions should be observable, maintainable, scalable and production-ready from architecture through release. Suggested revision exercise: sketch the web-to-backend flow for a hypothetical AI feature and explain component boundaries, failure handling and what you would observe in production.
Measurable AI quality
Posting: Validation combines functional QA, evals and tracing to make quality reproducible. Suggested revision exercise: define expected behavior for three example requests, then explain what each of QA, an eval and a trace would reveal.
Agentic development workflow
Posting: Regular use of coding-agent harnesses is a minimum qualification, and the role applies agentic techniques across the software development lifecycle. Suggested revision exercise: outline where an agent would assist in planning, implementation and testing, and where you would verify its output.
Product refinement and scoping
Posting: The engineer refines requirements with product stakeholders and prototypes with designers, PMs and engineers. Suggested revision exercise: turn a hypothetical AI feature request into a user journey, acceptance criteria, an initial prototype scope and questions for stakeholders.

Skills to revise

Build a web-to-backend feature
Suggested exercise: implement a small user journey using one web framework and one backend framework named in the posting, with TypeScript/JavaScript or Python as appropriate. This rehearses the stated full-stack and framework requirements; the posting does not require every listed option.
Implement a model-powered agent
Suggested exercise: build a small feature using a frontier model from one of the named providers; supply context, define a tool interaction and inspect the generated result. This directly rehearses the posting’s hands-on AI and agent-feature requirement.
Run QA, evals and traces
Suggested exercise: write functional checks and a repeatable set of example inputs and expected behaviors for the feature, then inspect traces when a case fails. This matches the stated responsibility to measure and reproduce quality.
Work with a coding-agent harness
Suggested exercise: use one named harness, such as Claude Code, Codex, Opencode or Pi, to plan a change, implement it and help test it; review the changes yourself. The posting calls for regular harness use during development.
Prototype from a stakeholder brief
Suggested exercise: write a short hypothetical stakeholder brief, identify unresolved requirements, build a narrow prototype and explain what you would prioritize next. This rehearses the stated work of refining concepts, scoping projects and collaborating on prototypes.

Your preparation checklist

Map evidence to the core requirements
Prepare a one-page inventory of examples, if available, covering professional web and backend programming, TypeScript/JavaScript or Python, a web framework, a backend framework, and AI features built with OpenAI, Anthropic or Google models. Note your specific contribution and results without overstating experience.
Rehearse a production feature walkthrough
Choose one relevant AI or agent-powered feature, if available, and explain the user journey, architecture, frontend and backend work, model integration, release decisions and observed outcome. The role spans the full stack from architecture through release.
Sketch a hypothetical AI user journey
As interview practice, diagram a software-buyer feature for G2’s marketplace; it is not an advertised project. Show how prompting, context, tools and model reasoning could serve the user, then identify scalability, maintainability and observability trade-offs.
Prepare a quality-validation plan
For the hypothetical feature, write a small set of functional QA cases, agent evaluation examples, success criteria and tracing fields. Be ready to explain how failures could be reproduced and investigated; the role explicitly calls for QA, evals and tracing.
Document an agent-assisted development workflow
Prepare a concrete account, if applicable, of how you use a coding-agent harness in planning, implementation, testing or maintenance. Include what you review yourself and how you verify generated changes; regular harness use is a stated qualification.
Practice refining a feature with stakeholders
Prepare an example, if available, of turning a product stakeholder’s concept into requirements with a designer, PM or engineer. Show the questions asked, a prototype or scope decision, and how priorities were agreed.
Select optional experience to discuss
If relevant, prepare brief evidence for end-user AI-generated content, multimodal or speech models, real-time full-duplex applications, or agent evals. The posting lists these as ways to strengthen an application, not minimum requirements.
Write focused questions for G2
Bring questions about which AI user journeys the team is prioritizing, how it measures agent quality in production, and how engineers collaborate with designers and PMs on scope. Ask rather than assume which models, tools or processes the team uses.

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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