Software Engineer – AI-Native Product Platform

Job DescriptionSoftware Engineer – AI-Native Product Platform

Company: KAVIA AI

Location: India (Remote)

Type: Full-time

Compensation: ₹16–25 LPA

About KAVIA AI

KAVIA AI is an enterprise software company building a production AI-native platform used today by real teams on real, large-scale codebases.

We are creating an AI-native workflow and reasoning layer for the entire software development lifecycle — from requirements and architecture to code generation, testing, CI/CD, and long-term evolution. Unlike point tools or copilots, KAVIA is designed to understand, reason about, and operate on entire systems over time, not just individual prompts or files.

At the core of the platform are:

• Agent-based orchestration systems that plan, execute, and validate work across the SDLC

• Enterprise knowledge graphs that represent code, architecture, requirements, and decisions

• Full lifecycle traceability, enabling teams to understand why software exists and how it evolves

KAVIA is already deployed in production environments and used by enterprise customers. As the platform and customer footprint grow, we’re hiring engineers to help scale, harden, and evolve the core systems that power KAVIA.

The Role

This is a hands-on software engineering role on an existing, production AI platform.

As a Software Engineer, you’ll work closely with senior engineers and the founding team to design, implement, and operate core backend systems, frontend interfaces, and AI-native workflows that are already used by enterprise customers.

You’ll contribute directly to foundational platform components, gain exposure to real architectural tradeoffs, and gradually take ownership of meaningful subsystems. This role is ideal for engineers who want to work on serious, long-lived systems and learn how modern AI-native platforms are built responsibly.

What You’ll DoBuild and Evolve the Platform

• Implement backend services and APIs that power KAVIA’s AI-native workflows

• Contribute to agent-based orchestration systems that plan and execute complex tasks

• Work with knowledge-graph-driven representations of code, workflows, and metadata

• Extend, refactor, and harden systems already running in production

Hands-On Engineering

• Write clean, well-tested, production-quality code

• Participate in design discussions and code reviews

• Debug issues across backend services, AI workflows, and system integrations

• Improve performance, reliability, and observability of core services

Production & Operations

• Deploy and operate services in cloud environments using containerized systems

• Support production systems used by enterprise customers

• Learn to reason about scalability, failure modes, and long-term system health

Collaboration & Growth

• Work closely with senior engineers and learn from real architectural decisions

• Translate well-defined requirements into robust implementations

• Gradually grow ownership across backend systems, AI workflows, and infrastructure

What We’re Looking ForRequired

• ~3+ years of professional software engineering experience

• Strong backend engineering fundamentals, especially with Python

• Experience building APIs and backend services used in production

• Experience working with React or Next.js-based frontend systems

• Familiarity with data storage systems (relational databases and modern data stores)

• Experience deploying and operating services in a cloud environment

• Comfort working in a fast-moving environment with real users and real constraints

Strong Plus

• Experience with Docker, Kubernetes, Terraform, and CI/CD pipelines

• Experience with MongoDB and AWS-based infrastructure

• Experience using AI tools as part of your own development workflow

• Experience operating production systems with uptime and reliability requirements

• Startup experience or ownership of systems beyond just feature delivery

Why This Role Is Interesting

• Work on a real, production AI platform — not prototypes or experiments

• Learn how AI-native systems are designed, built, and operated at scale

• Be close to architecture, product decisions, and long-term technical direction

• Solve hard problems at the intersection of AI, systems engineering, and enterprise software

• Build infrastructure and workflows that real engineering teams depend on every day