Description

Education: B.Tech / M.Tech / MS / PhD in Computer Science or related technical field

About the Role

We are seeking a senior AI Solution Architect who is passionate about remaining 100% hands-on with technology. This is a pure Individual Contributor (IC) position—there is zero people management, team oversight, or administrative overhead.

In this role, you will sit directly across the table from enterprise clients to architect production-grade GenAI systems, optimise LLM pipelines, scale compute infrastructure, and justify complex technical trade-offs in real time. If you thrive on rolling up your sleeves, writing code, and solving real-world AI execution bottlenecks, this role is built for you.

Key Responsibilities

• Enterprise Client Architecture: Interface directly with client technical leadership to translate business requirements into scalable, secure AI architectures.
• GenAI & Agentic System Design: Architect and build end-to-end multi-agent workflows, LLM orchestration layers, and RAG pipelines using frameworks like LangChain and LangGraph.
• Real-Time Technical Execution: Manage hands-on infrastructure scenarios, including GPU scaling, embedding model selection, vector indexing, fine-tuning evaluations, and model unlearning.
• API & Platform Integration: Define microservices patterns, serverless architectures, and REST/GraphQL/WebSocket endpoints to integrate AI services into enterprise software platforms.
• Security & Responsible AI: Implement robust data privacy, zero-trust access controls (IAM/RBAC), encryption standards, and AI governance guardrails across multi-cloud environments.

What We Are Looking For

• Total Experience: 10+ years of solid software engineering and distributed systems background.
• GenAI Expertise: 3–4+ years of direct, hands-on experience building production GenAI applications, vector search architectures, and fine-tuning pipelines.
• Frameworks & Tools: Deep implementation knowledge of LangChain, LangGraph, Hugging Face Transformers, and agentic design patterns.
• Data & Vector Stores: Practical experience with vector databases (Pinecone, Weaviate, pgvector, Qdrant) alongside relational and NoSQL databases.
• Cloud Architecture: Hands-on deployment experience across AWS (Bedrock, SageMaker), Azure (OpenAI Service, AKS), or GCP (Vertex AI). Cloud architect certifications are a strong plus

Key Skills

Education

Any Graduate