Gen AI Architect

What You'll Do

• Design & Implement Agentic AI Systems
• Architect and build multi-agent, goal-driven, autonomous AI systems using frameworks such as:
• AutoGen
• CrewAI
• LangGraph
• 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.

Expertise You'll Bring

• 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.