Associate Technical Architect

Quantiphi Analytics Solution Pvt Ltd; Bengaluru; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Quantiphi Analytics Solution Pvt Ltd in Bengaluru seeks an Associate Architect SD who can lead a team and drive engineering work with heavy use of generative AI. You will own problems from initial brief to production, guiding AI-driven delivery while collaborating with the Tech Architect and product teams.

You will work across frontend, backend, and data, leveraging cloud platforms (AWS/GCP), containers, and CI/CD to ensure production readiness and robust observability.

Qualifications

• Requires 5+ years of software leadership and hands-on architecture work.
• Experience leading teams and delivering end-to-end features using AI.
• Familiarity with cloud platforms (AWS/GCP) and CI/CD pipelines.

Responsibilities

• Take problems from first description to delivered features using generative AI.
• Architect and review frontend, services, and data models.
• Assess AI output for risk, latency, and cost; ensure production readiness.
• Collaborate with product managers and engineers to deliver roadmaps.
• Lead across multiple layers of the stack and drive architectural decisions.

Skills

Generative AI

Leadership

Architectural thinking

CI/CD

Observability

Tools

AWS

GCP

Docker

Claude Code

Kiro

GitHub Copilot

SpecKit

Job description

Associate Technical Architect

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth. If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

Role Summary

We are looking for a Associate Architect SD who has led a team and who uses generative AI heavily to deliver day-to-day engineering work. This is a builder's role with an architect's habits. You will own problems from the vague first description through to something running in production, and you will do it by directing AI rather than by typing every line yourself. You will work closely with the Tech Architect as a thinking partner across product and engineering.

What You Should Know

Working with AI (mandatory)

• You should be able to point to real delivery, not experiments or weekend reading.
• Spec-driven development. You have worked from a written spec that an agent executes against. Frameworks such as GSD, BMAD or SpecKit, or an equivalent approach your team built in-house.
• Harness engineering. Hands-on with an agent harness such as Claude Code, Kiro or GitHub Copilot. You have configured and shaped one, not only typed into it.
• Sub-agents. You understand how to split work across agents, and where that helps and where it makes things worse.
• Skills and custom agents. You have built reusable skills or custom agents for your own workflow rather than using only what came out of the box.
• Judgment about all of the above. You know where these methods break down, because you have hit it.
• Good to have, and worth telling us about: context and token efficiency, evaluating agent output at scale, cost control, or your own tooling built on top of a harness.
• This field moves faster than any list we can write, so if you have gone further than the bullets above, say where.

Engineering breadth

• We care about range more than depth in any single technology. Comfortable across layers: frontend behaviour, backend services and API design, and how data is modelled and stored.
• React, TypeScript, Node, Python, relational and document databases are the kind of ground we work on.
• You should be able to operate on most of it.
• Able to hold an architecture discussion and design a system, rather than recall framework specifics.

Cloud and delivery

• Hands-on with a major cloud provider, AWS or GCP.
• Comfortable with Docker.
• Able to set up CI/CD pipelines for automated testing and deployment.
• Familiar with logging and observability tooling.

Experience Required

All three are required. If you do not meet all three, this is not the right role for you right now. At least five years building software professionally. You have led a team. Formal reports or a tech lead role with no reporting line both count. What matters is that you have actually led, not what the title said. You have worked across layers, not inside one. We are not looking for a specialist in a single part of the stack.

What You Will Own

End-to-end delivery

Take a problem from first description to delivered feature. Turn vague requirements into a technical plan, and surface risks and trade-offs while they are still cheap to act on. You drive that delivery with generative AI. Spec-driven development and agent harnesses are how the work gets done here, not a side experiment.

Architecture thinking

Hold the architecture in your head: how the frontend, the services and the data layer fit together, and where each will strain. You need that so you can work with an agent to reach the right design, review what it proposes, and catch the choice that will hurt in six months. You are not expected to hand-build every layer yourself.

Technical and product judgment

You have built real software before, and it shows in the questions you ask. An agent will happily produce code that works on a clean slate, and most of our work is not a clean slate. Reading an existing system, understanding why it is shaped the way it is, and changing it without breaking things is the job. You should also be able to tell where AI genuinely belongs in a product and where it does not, with a working sense of the cost, latency and failure modes involved.

Quality and production readiness

You set the standard for what ships. When most of the code is agent-written, the skill is not typing it cleanly. It is knowing what good looks like and catching what is wrong in review.

Working with people

You work closely with the Tech Architect, product managers and other engineers. You raise blockers early and bring options with them.

Why This Role Is Different

This is not a traditional full-stack engineering job, and the difference is not cosmetic. The engineers who do well here think in user journeys, systems and outcomes rather than tickets. They own problems rather than tasks. And they treat AI as the way the work gets done, not as a tool they occasionally reach for. If that describes how you already work, this will feel like the obvious next role. If it does not, it will be a frustrating one. If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us !

Experience Level

Mid Level