Gen AI Architect

We are seeking a highly experienced Agentic AI / Generative AI Architect with 15+ years of software and solution architecture experience, combined with handson expertise in Data Science, Machine Learning, and modern agent-based AI systems. This role requires deep technical leadership in designing, implementing, and governing advanced multi-agent AI ecosystems, RAG pipelines, cloud-native AI platforms, and GenAI engineering practices. The ideal candidate will drive endtoend solution architecture for enterprise-grade AI applications while ensuring scalability, robustness, security, and operational excellence.

What You''''ll Do:

• Design & Implement Agentic AI Systems

• Architect and build multi-agent, goal-driven, autonomous AI systems using frameworks such as:

• AutoGen

• LangGraph

• CrewAI

Create intelligent agent ecosystems supporting orchestration, reasoning, and collaborative task execution.

Prompt Engineering & LLM Expertise

Apply advanced prompt engineering techniques including:

• Few-shot prompting
• Chain-of-thought reasoning
• Prompt templates

Optimize prompt flows for deterministic, scalable LLM-driven systems

Cloud-Native AI Architecture

Design and deploy AI/LLM systems on cloud platforms such as AWS Bedrock, Azure OpenAI, Google Vertex AI, etc.

Ensure solutions meet enterprise NFRs including performance, security, cost-optimization, and availability.

RAG Pipelines, Vector Databases & MCP

Architect and deploy RAG pipelines using vector databases such as:

• Pinecone
• Weaviate
• ChromaDB
• FAISS

Implement MCP Servers and Agent-to-Agent (A2A) communication frameworks.

LMOPs / GenAIOPs

Implement end-to-end operational pipelines for GenAI applications including:

• Continuous integration & deployment
• Model monitoring & drift detection
• Logging, observability, and troubleshooting mechanisms

Establish governance models, reusable patterns, and GenAI best practices.

Application & Microservices Architecture

Design microservices-based systems using Spring Boot, REST APIs, and secure API design patterns.

Implement API security, versioning, and distributed system governance.

Architect cloud-native applications using AWS/Azure/Google Cloud Platform, Spring Cloud, PCF, or equivalent.

Collaboration & Leadership

Work closely with Data Scientists, Product Owners, Business SMEs, and Engineering teams.

Lead end-to-end solution architecture for enterprise AI initiatives.

Conduct technical presentations, architectural reviews, and stakeholder communication.

• 5+ years in software/solution architecture.

• Proven experience as a Data Scientist or ML Engineer with exposure to agentic AI systems.

• Experience designing multi-agent systems using AutoGen, LangGraph, CrewAI, etc.

• Strong understanding of cloud AI platforms (Bedrock, Azure OpenAI, Vertex AI).

• Hands-on experience with AI Code Assist tools such as:

• GitHub Copilot

• Windsurf

• Cursor

• AWS Q

Expertise in Vector Databases, RAG pipelines, MCP, and multi-agent communication.

Strong proficiency in Python (preferred), and optionally Java/Node.js.

Experience with microservices, Spring Boot, REST APIs, API security, and versioning.

Proficiency in Docker, Kubernetes, CI/CD pipelines.

Strong grasp of design patterns and architecture principles.

Deep understanding of cloud-native design and distributed systems.

Experience designing AI systems that meet NFRs: scalability, security, performance, maintainability.

Exceptional communication and presentation skills.

Ability to articulate complex AI concepts to technical and non-technical audiences.

Strong leadership, problem-solving mindset, and strategic thinking abilities.

Ability to collaborate with cross-functional teams to translate business needs into AI-powered solutions.