Technical Architect

Role Overview
We are looking for a highly accomplished Generative AI Architect to lead the design, development, and deployment of enterprise-scale AI solutions. The ideal candidate will drive AI transformation initiatives by architecting solutions leveraging Large Language Models (LLMs), Agentic AI, RAG (Retrieval-Augmented Generation), Multi-Agent Systems, AI Orchestration Frameworks, and Cloud-Native Platforms.
This role requires a blend of AI architecture, software engineering, cloud architecture, enterprise integration, and stakeholder leadership to deliver innovative and scalable Gen AI products.
Key Responsibilities
AI & Solution Architecture
Define enterprise-wide Generative AI strategy and architecture roadmap.
Design end-to-end GenAI solutions aligned with business objectives.
Architect scalable, secure, and production-ready AI platforms.
Drive architecture governance, design reviews, and technical best practices.
Generative AI & Agentic AI
Design and implement solutions using GPT, Claude, Gemini, Llama, or equivalent LLMs.
Architect Agentic AI systems with planning, reasoning, memory, orchestration, and tool execution capabilities.
Develop AI agents capable of interacting with enterprise applications through APIs and tools.
Define prompt engineering, evaluation, guardrails, and responsible AI standards.
RAG & Knowledge Systems
Design Retrieval-Augmented Generation (RAG) architectures.
Build document ingestion, chunking, embedding, indexing, and retrieval pipelines.
Implement vector databases and semantic search capabilities.
Optimize relevance, grounding, and hallucination reduction techniques.
Enterprise Integration
Integrate AI solutions with enterprise applications, databases, APIs, and business workflows.
Design MCP (Model Context Protocol) and function-calling frameworks for AI-driven automation.
Enable conversational AI, intelligent assistants, and business process automation.
Cloud & Platform Engineering
Architect AI workloads on AWS, Azure, or GCP.
Design containerized deployments using Kubernetes and Docker.
Implement CI/CD, MLOps, LLMOps, monitoring, and governance frameworks.
Ensure scalability, resiliency, observability, and security of AI platforms.
Leadership & Stakeholder Management
Partner with business leaders to identify and prioritize AI use cases.
Lead architecture discussions with clients and executive stakeholders.
Mentor engineering teams and drive AI capability development.
Provide technical leadership for POCs, pilots, and enterprise implementations.
Required Skills
Generative AI
Large Language Models (LLMs)
Generative AI
Agentic AI
Multi-Agent Systems
AI Agents
Prompt Engineering
Function Calling
MCP (Model Context Protocol)
AI Governance
RAG & AI Frameworks
RAG Architecture
LangChain
LangGraph
CrewAI
LlamaIndex
Semantic Kernel
Vector Databases
Programming
Python (Mandatory)
Java/Spring Boot (Preferred)
REST APIs
FastAPI / Flask
Cloud & DevOps
AWS / Azure / GCP
Kubernetes
Docker
Terraform
Jenkins / GitHub Actions
CI/CD Pipelines
Data Technologies
Pinecone
Weaviate
Milvus
ChromaDB
Elasticsearch/OpenSearch
SQL & NoSQL Databases

Work arrangement
Hybrid