Product AI Architect

Indegene; Bengaluru; Hybrid; INR 3,000,000 - 6,000,000; Full time

Job summary

Indegene seeks a Hands-On Full-Stack Technology Architect with AI and LLM expertise to lead design and implementation of scalable AI platforms. The role combines frontend/back-end work with building and integrating LLM-enabled features, opinions on architecture, and cross-team collaboration.

You will prototype AI systems, implement vector DB pipelines, and ensure secure, scalable production delivery using modern DevOps practices and IaC tooling.

Qualifications

• Hands-on design/architecture for AI platforms
• Experience with microservices and cloud-native patterns
• Proficiency in frontend and backend tech stacks
• Expertise in LLM integrations and orchestration
• Knowledge of LangChain, embeddings, and vector stores

Responsibilities

• Define scalable cloud-native architectures for AI platforms
• Architect and prototype AI systems and document API contracts
• Build end-to-end architectures and deploy solutions
• Develop reusable LLM-enabled components and reference implementations
• Integrate LLM APIs and vector DB pipelines; collaborate with data scientists
• Mentor engineers and collaborate with product/design teams

Skills

JavaScript/TypeScript

Python

Go

React/Angular

REST/GraphQL

Event-driven

AWS/Azure

Docker/Kubernetes

Infrastructure as Code

LangChain / LlamaIndex

Embeddings / Vector DBs

Prompt engineering

Tools

Terraform

Helm

Kubernetes

Pinecone

FAISS

Weaviate

SageMaker

Vertex AI

HuggingFace

Job description

Role & responsibilities

Hands-On Full-Stack Technology Architect with AI and LLM expertise is a senior technical leader who designs scalable, intelligent systems while actively contributing across the tech stack. This role is ideal for someone who combines modern web and backend development with deep experience in integrating and scaling Large Language Model (LLM) platforms such as OpenAI, Claude, Mistral, or custom models via Hugging Face, AWS Bedrock, or Vertex AI.

Architecture & Design

Define robust, scalable, cloud-native, cloud agnostic architectures for AI platforms Architect and prototype AI systems/platforms Design and document system architecture (component diagrams, API contracts, deployment topology).

Champion modern design patterns (microservices, micro frontends, event-driven, DDD, etc.).

Build

composable architecture patterns for integrating LLM APIs with microservices and UI layers.

Create end-to-end architectures, build distributions, environments and deploy solutions.

Hands-On Development

Build and integrate reusable components, LLM-enabled APIs, and workflows with stateful orchestration where needed (e.g., using LangChain or semantic pipelines).

Develop and iterate on PoCs and reference implementations of LLM-based features.

Write maintainable, high-quality code in both frontend (React) and backend (Python ,Node.js, Java, Go) technologies.

Build reusable UI components, design systems, and frontend microservices where applicable. Create proof-of-concepts and reference implementations to de-risk architectural decisions.

Contribute to CI/CD automation, DevOps pipelines, and infrastructure-as-code setups (Terraform, Helm).

Architect and implement responsive UIs with state management (Redux, Zustand, or similar).

Integrate RESTful and GraphQL APIs, implement frontend performance tuning and security best practices (XSS, CSP, etc.). Define and enforce front-end development standards and reusable design patterns.

LLM Engineering & Integration

Integrate LLMs via OpenAI, Claude, Mistral, Vertex AI, or AWS Bedrock APIs.

Apply techniques like prompt engineering, embeddings, RAG (retrieval-augmented generation), and fine-tuning where applicable. Design and implement vector database-backed search pipelines using Pinecone, FAISS, Weaviate, or Vespa. Collaborate with data scientists and MLOps teams to bring custom LLMs to production securely and reliably.

Team Collaboration & Leadership

Work with product owners, UX/UI designers, QA, and devs across teams to shape and implement feature architecture. Mentor engineers across levels through pair programming, design reviews, and architectural guidance.

Participate in sprint planning, story breakdowns, and estimation with a technical mindset. Partner with BU teams in driving the collaboration mandates. Communication: Effectively communicate findings and recommendations to both technical and non-technical audiences.

Required Skills and Qualifications

Engineering Hands on proficiency in Design and Architecture of the platforms Good awareness of the design patterns & implementation experience Handson Proficiency in JavaScript/TypeScript, Python, or Go. Experience with React or Angular on the frontend. REST/GraphQL API design and integration. Familiarity with event-driven architectures and async processing (Kafka, RabbitMQ,etc.). Microservices Strong with AWS/ Azure, containerization (Docker, Kubernetes), and Infrastructure-as Code . Must have worked on the production-ready solutions and be abreast with the scaling and complexity solutions involved in creating enterprise solutions. LLM & AI Platforms Experience integrating LLM APIs (OpenAI, Claude, Mistral, Cohere, etc.). Knowledge of LangChain, LlamaIndex, or Semantic Kernel for orchestration. Hands-on with embeddings, vector databases (Pinecone, FAISS, etc.), and prompt engineering. Familiarity with AI/ML deployment platforms (SageMaker, Vertex AI, Hugging Face, etc.).