AI Architect

ID Softsource; Coimbatore District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

ID Softsource in India is seeking an experienced AI Architect with 3+ years of hands-on experience in AI/GenAI architecture, agentic AI, LLM applications, and production AI systems. You will translate business requirements into scalable AI solutions and lead end-to-end AI design and deployment.

You will design multi-agent architectures, RAG and MCP-enabled tool ecosystems, and oversee cloud-based implementation, ensuring security and reliability across the development lifecycle.

Qualifications

• 3+ years of experience in AI/GenAI architecture, agentic AI, LLM applications, and production AI systems.
• Translate business and functional requirements into scalable, production-ready AI solutions.
• Design end-to-end AI/GenAI architecture including data flows, agent flows, API integrations, and deployment architecture.

Responsibilities

• Design end-to-end AI/GenAI architecture including data flows, agents, APIs, and deployment.
• Translate business needs into scalable, production-ready AI solutions.
• Lead design of Agentic AI systems, including planning, reasoning, and orchestration.
• Define multi-agent architectures with SupervisorWorker patterns and recovery mechanisms.
• Evaluate LLMs, RAG, embeddings, and integration with enterprise services.
• Provide technical leadership to AI/ML teams and ensure security and observability.

Skills

AI architecture

GenAI

LLMs

Agentic AI

Production AI systems

Cloud platforms

Docker

APIs

Tools

Docker

APIs

Vector search

RAG

MCP

Job description

We are looking for an experienced AI Architect with 3+ years of experience in AI/GenAI architecture, agentic AI, LLM applications, and production AI systems. The candidate will be responsible for translating business requirements into scalable AI solutions, designing end-to-end AI/agent architectures, and providing technical leadership throughout the development and deployment lifecycle.

Roles & Responsibilities

• Design end-to-end AI/GenAI architecture, including system components, data flows, agent flows, API integrations, and deployment architecture.
• Translate business and functional requirements into scalable, secure, and production-ready AI solutions.
• Design and implement Agentic AI systems, including AI agents, planning, reasoning, task decomposition, routing, and orchestration.
• Design multi-agent architectures, including SupervisorWorker patterns, agent delegation, handoffs, coordination, and failure/recovery mechanisms.
• Evaluate and select appropriate LLMs/models based on use cases, performance, cost, latency, and reliability.
• Design and implement RAG architectures, vector search, embeddings, retrieval strategies, and knowledge-based AI solutions.
• Architect tool calling, function calling, API integrations, and enterprise service integrations for AI agents.
• Design and integrate MCP (Model Context Protocol)-based tool ecosystems and connect AI agents with enterprise applications and services.
• Define AI workflow and state-management architectures, including agent memory, context management, and conversation state.
• Establish AI evaluation, guardrails, observability, monitoring, security, and reliability practices for production systems.
• Prepare and maintain HLD/LLD, architecture diagrams, technical specifications, and solution documentation.
• Define AI project technical roadmaps, milestones, delivery plans, and development priorities.
• Work closely with developers, project managers, business teams, and other technical stakeholders to ensure successful project delivery.
• Provide technical leadership and guidance to AI/ML development teams and support architectural decision-making.
• Review existing AI solutions and identify opportunities for performance, scalability, cost, and reliability improvements.
• Lead the deployment of AI solutions using Cloud platforms, Docker, APIs, and production infrastructure.
• Ensure development follows appropriate coding standards, security practices, architecture principles, and deployment guidelines.
• Stay updated with emerging technologies in GenAI, LLMs, Agentic AI, MCP, RAG, and AI orchestration frameworks.

Good to Have

• Experience in real-time Voice AI / Conversational AI
• Experience with voice agents and real-time AI workflows
• Experience integrating AI systems with enterprise applications
• Experience leading or mentoring AI development teams