New opportunity

Software Engineer (Fullstack/AI) - Work from Office

AlgoUniversity · Bengaluru, India

About this role

Build AI-agent and full-stack learning products for AlgoUniversity and Tensor School, working directly with the CTO. The products include software-development and learning agents, coding-agent harnesses, and platforms ranging from launch-stage products to systems serving up to 1 million users.

Responsibilities

Develop AI agents and harnesses, tools that use coding agents, and end-to-end products across frontend, backend, APIs, and infrastructure.
Build reliable production systems for learners and take new capabilities from an idea through deployment.
In the first 30 days, help launch the next version of the AI-agent harness to 1 lakh users; by day 60, scale it, improve reliability, and expand its capabilities alongside the CTO.

Requirements and application:

Strong software-engineering fundamentals, experience building full-stack projects or products, and the ability to learn quickly and work across the stack.
Familiarity with Python, JavaScript, Docker, Azure, or AWS, plus practical experience using coding agents such as Codex or Claude Code. The listing also names Django and React as skills.
Fresh graduates are welcome. The posting header says “Fresh graduates to 4 years,” while the requirements say “fresh graduate or up to 2 years of software-development experience”; a separate listing field says “1+ years.”
Applicants are encouraged to show a deployed product, AI agent, open-source contribution, or substantial side project and explain what they built, why, and which parts they owned.

Arrangement and compensation:

Full-time, in-person role primarily in Bengaluru; Hyderabad is available depending on the team. Salary is ₹10 lakh–₹25 lakh per year. Engineering perks include access to current AI models, coding agents, developer tools, and company-provided unlimited token access for engineering work. The listing’s visa field states “US citizen/visa only.”

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

Skills for this role

AI AgentsCoding agentsCodexClaude CodePythonJavaScriptDockerMicrosoft AzureAmazon Web Services (AWS)DjangoReactFull-stack developmentAPI developmentSoftware engineering

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

Launch and scale the AI-agent harness
The posting sets a first-30-day goal of launching the next harness version to 1 lakh users, followed by scaling it, improving reliability, and expanding capabilities by day 60. Prepare to explain how you would sequence a launch and respond to production issues.
Build across the full product stack
The role spans frontend, backend, APIs, and infrastructure, with an expectation of taking an idea to working production software. Prepare a concrete example that traces your decisions from product need through implementation and deployment.
Make coding agents useful in real products
The team builds software-development and learning agents, harnesses, and tools that use coding agents. Practical experience with tools such as Codex or Claude Code is requested. Be ready to describe what an agent did, what you controlled, and how you checked its output.
Demonstrate ownership through shipped work
The posting asks applicants to show a deployed product, AI agent, open-source contribution, or substantial side project and explain what they built, why, and which parts they owned. Prepare one concise account with clear boundaries around your contribution.

Likely challenges to think through

Balancing launch speed with reliability
Preparation hypothesis: The 30-day launch and 60-day reliability goals could require shipping before every improvement is complete. Prepare to discuss how you would choose a safe initial scope, observe failures, and prioritize fixes after release; the posting does not specify a rollout method.
Controlling agent output without slowing development
Preparation hypothesis: Coding-agent tools may accelerate implementation while producing results that need verification, especially in learner-facing systems. Prepare an example of where you would use an agent and where you would require tests, review, or other checks.
Working across layers as needs change
Preparation hypothesis: Moving between frontend, backend, APIs, and infrastructure while developing new capabilities with the CTO could make prioritization difficult. Prepare to explain how you would identify the blocking layer and make trade-offs without treating every part of the stack as equally urgent.

Practice your answers

Technical practice

How would you structure a harness for a software-development or learning agent?

What to coverThe role includes building AI agents and coding-agent harnesses. Preparation suggestion: explain how you would separate agent instructions, tool access, execution, and results, and how you would handle a failed or incomplete run.

How would you use a coding agent to build a feature without trusting its output blindly?

What to coverThe posting asks for practical experience with coding agents such as Codex or Claude Code. Preparation suggestion: discuss task scoping, reviewing generated changes, testing, and deciding when to revise or discard the output.

How would you evaluate whether a learning agent is useful to students?

What to coverThe advertised products include learning agents for students. Preparation suggestion: distinguish a plausible answer from one that helps a learner, and describe examples you would test and signals you would examine before expanding use.

How would you build a learner-facing feature across frontend and backend?

What to coverThe role covers end-to-end products across frontend, backend, APIs, and infrastructure. Preparation suggestion: walk through the user interaction, data flow, error states, and tests, explaining where you would keep the implementation simple.

How would you design an API for a long-running agent task?

What to coverAPI development and AI-agent products are both part of the stated work. Preparation suggestion: explain how a client would start a task, observe progress, receive a result or failure, and avoid confusion if it retries a request.

How would you take a full-stack change from local development to production?

What to coverThe posting calls for working production software and familiarity with Docker, Azure, or AWS. Preparation suggestion: describe a deployment path using tools you actually know, including configuration, tests, and a way to detect or reverse a bad release.

What would you examine before scaling an AI-agent harness to more users?

What to coverThe posting states a first-30-day launch target of 1 lakh users and a subsequent goal to scale the harness and improve reliability. Preparation suggestion: discuss workload measurement, bottlenecks, concurrency, and the trade-off between adding capacity and reducing work per request.

How would you investigate intermittent failures in a production agent workflow?

What to coverBuilding reliable production systems for a growing learner community is an advertised responsibility. Preparation suggestion: explain how you would reproduce a failure, inspect traces or logs, isolate the failing component, and verify that a fix holds.

How would you decide what to ship first for a new agent capability?

What to coverThe role involves taking capabilities from idea to deployment and expanding what the harness can do. Preparation suggestion: define a small useful version, identify its main technical risk, and explain what evidence would justify expanding it.

Behavioural practice

Tell me about something you built and which parts you owned.

What to coverThe posting asks applicants to explain what they built, why, and what they owned. Preparation suggestion: use a real deployed product, agent, open-source contribution, or substantial project if available; give the situation, your actions, and the result without overstating your contribution.

Describe a time you had to learn an unfamiliar part of a stack quickly.

What to coverThe role calls for learning quickly and working across different parts of the stack. Preparation suggestion: give a real situation, explain how you found and checked what you needed to learn, and state the outcome.

Describe a time you developed a technical approach with a close collaborator.

What to coverThe posting says this engineer will build new capabilities directly alongside the CTO. Preparation suggestion: use a real example to explain the situation, how you discussed alternatives or handled disagreement, what you did, and the result.

Tell me about a time you balanced shipping with reliability.

What to coverThe stated first-60-day goals pair launching an agent harness with scaling and reliability improvements. Preparation suggestion: describe a real deadline or release, the risks you identified, the safeguards you chose, and what happened afterward.

Sharpen your knowledge

Topics to prepare

AI-agent harness design
The role builds software-development and learning agents and aims to launch and scale an AI-agent harness. Sketch how a harness would run a coding task, check its result, and handle a failed run; explain your design choices. The sketch is a preparation exercise, not a specified employer design.
Software-engineering fundamentals
Strong fundamentals are an explicit requirement. Revisit a small feature you have built and explain its data flow, edge cases, error handling, and testing choices.
Full-stack system boundaries
The posting calls for products spanning frontend, backend, APIs, and infrastructure. Diagram a learner-facing feature and trace a request through each layer, including where errors are reported.
Reliability and scale
The stated first-60-day goals are to launch a harness to 1 lakh users, then scale it and improve reliability. As a preparation exercise, identify likely failure points in a high-traffic agent workflow and explain how you would detect and prioritize them; these techniques are suggestions, not specified requirements.
Idea-to-production trade-offs
The role asks for the ability to take a problem from idea to working production software. Practice outlining a small feature's scope, first release, validation, and next improvement, explaining what you would defer.

Skills to revise

Use a coding agent with verification
Practical experience with coding agents such as Codex or Claude Code is explicitly requested. Use one to make a bounded code change, then review the diff, run checks, and explain what you accepted or corrected.
Build an end-to-end feature
Full-stack project experience is requested, and the role spans frontend, backend, and APIs. Implement a small learner-facing flow with a frontend action and backend API; test the success and error paths. Python and JavaScript are named familiarity areas, while Django and React appear in the page's skills list.
Package and run a service
Docker and Azure or AWS are named familiarity areas, not a requirement to use every platform. As practice, package a small API with Docker, run it locally, and describe what would need configuring before deployment.
Exercise failure handling
Reliable production systems and harness reliability are explicit priorities. Add a failing-input and an interrupted-run case to a small agent or API project, verify its behavior, and describe how you would investigate a failure in use.
Explain a built artifact and ownership
The posting encourages showing a deployed product, AI agent, open-source contribution, or substantial side project. Prepare a concise walkthrough of one artifact: why it was built, what it does, which parts you owned, and one decision you would change.

Your preparation checklist

Select one substantial build to present
Prepare a demo or repository for a deployed product, AI agent, open-source contribution, or substantial side project. The posting asks applicants to explain what they built, why, and which parts they owned.
Rehearse an end-to-end project walkthrough
Trace a real project from idea to deployment, covering frontend, backend, APIs, and infrastructure where applicable. Identify your decisions, trade-offs, tests, and lessons without claiming work you did not do.
Show how you use coding agents
Prepare one concrete example using a coding agent such as Codex or Claude Code: the task, what the agent produced, how you checked its output, and what you changed. Practical coding-agent experience is listed as a requirement.
Practice an AI-agent harness design exercise
Sketch a hypothetical learner-facing agent harness and explain its components, failure handling, and tests. Use the posting’s first-30-day launch target of 1 lakh users and day-60 reliability and scaling goals as design prompts, not as claims about the existing system.
Prepare for the stated interview stages
The page lists a take-home assignment followed by assignment evaluation and a technical interview with the CTO, then a CEO interview. Practice explaining your implementation choices and reviewing your own work; the exact assignment is not specified.
Write focused questions for the team
Bring questions about the harness’s current state, how launch and reliability will be measured, collaboration with the CTO, and which team determines whether the in-person location is Bengaluru or Hyderabad.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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