GyanSys - Generative AI Architect

GyanSys Inc.; Bengaluru; On-site; INR 3,000,000 - 4,500,000; Full time

Job summary

GyanSys Inc. seeks a highly experienced Principal Solution Architect in Bengaluru to spearhead the deployment of enterprise-scale agent-driven AI systems. You will drive innovation in multi-agent architectures and Azure solutions while mentoring senior engineers.

Ideal candidates will have over 12 years of software engineering experience and expertise in agentic frameworks and generative AI. This role requires a visionary approach to lead the design and ensure the success of production-grade autonomous AI systems.

Qualifications

• 12+ years of experience in software engineering / AI / ML.
• 3+ years in architect or technical leadership roles.
• Experience in building and deploying agentic AI systems in production.
• Strong programming skills in Python.
• Experience with real-time/event-driven systems.

Responsibilities

• Define and own the architecture for multi-agent systems.
• Lead design and deployment using agentic frameworks.
• Establish patterns for tool/function calling and integration.
• Architect enterprise RAG frameworks.
• Build reusable AI platforms and frameworks.

Skills

Agentic AI & Multi-Agent Systems

Generative AI (LLMs, prompt engineering, fine-tuning)

Retrieval-Augmented Generation (RAG)

Microsoft Azure AI & Data Platform

System Design & Distributed Architecture

Data Engineering & Pipelines

AI Evaluation, Observability & Guardrails

Stakeholder Management & Technical Leadership

Tools

LangGraph

LangChain

AutoGen

CrewAI

Semantic Kernel

Azure OpenAI

Azure AI Foundry

Job description

We are seeking a highly experienced Principal Solution Architect Agentic AI & GenAI to lead the design and deployment of enterprise-scale, agent-driven AI systems.

This role will drive innovation across multi-agent architectures, LLM orchestration platforms, and Retrieval-Augmented Generation (RAG) on Microsoft Azure, with measurable business impact across enterprise domains.

The ideal candidate combines deep technical expertise in agentic frameworks with architectural vision and has successfully delivered production-grade autonomous AI systems at scale.

Key Responsibilities

• Define and own the architecture for multi-agent systems including planner/executor patterns, tool-use orchestration, agent-to-agent communication, and human-in-the-loop workflows.
• Lead design and deployment using agentic frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, and Azure AI Agent Service.
• Architect agent memory, state management, and long-running workflow patterns (short-term, long-term, episodic memory).
• Design agent evaluation, guardrails, and safety frameworks for autonomous decision‑making.
• Establish patterns for tool/function calling, MCP (Model Context Protocol) servers, and enterprise system integration from agents.

Azure-First GenAI Platform Architecture

• Own end-to-end architecture on Microsoft Azure, leveraging:
• Azure OpenAI Service, Azure AI Foundry, Azure AI Agent Service.
• Azure AI Search (vector + hybrid retrieval), Cosmos DB, Azure SQL.
• Azure Kubernetes Service (AKS), Azure Functions, Container Apps.
• Azure API Management, Event Grid, Service Bus.
• Azure Monitor, Application Insights for AI observability.
• Design secure, scalable, and cost-efficient AI solutions aligned with Azure Well-Architected Framework.
• Lead architecture decisions across model selection, fine-tuning, evaluation, and deployment within the Azure ecosystem.

RAG & Knowledge Systems

• Architect and implement enterprise RAG frameworks using Azure AI Search and vector databases.
• Design ingestion pipelines for structured and unstructured data (PDFs, documents, enterprise data).
• Optimize retrieval strategies: hybrid search, semantic re‑ranking, query rewriting, and chunking strategies.
• Integrate RAG as a tool/capability within agentic workflows.

Platform & Engineering Excellence

• Build reusable AI platforms and frameworks to accelerate solution delivery across teams.
• Drive adoption of microservices, event-driven architectures (Kafka / Azure Event Hubs), and API-first design.
• Ensure robustness through monitoring, evaluation frameworks, tracing (LangSmith, Langfuse, Azure AI evaluations), and feedback loops.

Model Strategy & Optimization

• Evaluate and deploy LLMs across Azure OpenAI (GPT family), open-source models (Llama, Mistral), and fine-tuned variants.
• Implement prompt engineering, guardrails, and hallucination mitigation strategies.
• Optimize for latency, cost, token efficiency, and performance in production agent workflows.

Governance & Responsible AI

• Establish best practices for AI safety, explainability, and compliance for autonomous agents.
• Implement data governance, privacy, and security standards (Azure Purview, Microsoft Defender for Cloud).
• Define human-oversight patterns for high‑stakes agentic decisions.

Leadership & Mentorship

• Mentor senior engineers and architects in agentic AI and GenAI best practices.
• Drive technical direction across multiple teams and projects.
• Influence leadership on AI strategy and roadmap.

Required Qualifications

• 12+ years of experience in software engineering / AI / ML.
• 3+ years in architect or technical leadership roles.
• Proven hands‑on experience building and deploying agentic AI systems (multi‑agent orchestration, tool-use, autonomous workflows) in production.
• Strong expertise with agentic frameworks: LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or equivalent.
• Generative AI & LLMs: prompt engineering, function calling, fine‑tuning.
• RAG architectures and vector databases (Azure AI Search, Pinecone, Weaviate, etc.).
• Microsoft Azure: Azure OpenAI, Azure AI Foundry, AKS, and the broader Azure data/AI ecosystem.
• Distributed systems and scalable backend architecture expertise.
• Proven experience delivering production AI systems at enterprise scale.
• Strong programming skills in Python; experience with ML frameworks and API design.
• Experience with real-time/event-driven systems (Kafka, Azure Event Hubs, streaming pipelines).

Key Skills

• Agentic AI & Multi-Agent Systems.
• Generative AI (LLMs, prompt engineering, fine‑tuning).
• Retrieval-Augmented Generation (RAG).
• Microsoft Azure AI & Data Platform.
• System Design & Distributed Architecture.
• Data Engineering & Pipelines.
• AI Evaluation, Observability & Guardrails.
• Stakeholder Management & Technical Leadership.