Gen AI Architect

Role: Gen AI Architect

Location: NJ, Jersey City

Experience: 12 to 15 Years

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.

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.