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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Gen AI Architect - **Company:** PERSISTENT - **Location:** Bridgewater, NJ, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Engineering, Continuous Integration, Cursor (Graphical User Interface Elements), Software Design Patterns, Distributed Systems, Monitoring of Systems, Python (Programming Language), Machine Learning, Node.Js, Management of Software Versions, Data Logging, Spring Cloud, GitHub Copilot, Large Language Models, Multi-Agent Systems, Prompt Engineering, Spring-boot, IT Architecture, Software Security, Generative AI, AI Platforms, Kubernetes, Virtual Agents, Restful APIs, Network Server, Docker, Microservices - **Published:** May 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=9b4e711049faaf44 ## About the Role We are seeking a highly experienced Agentic AI / Generative AI Architect with 15+ years of software and solution architecture experience, combined with hands-on 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 end-to-end solution architecture for enterprise-grade AI applications while ensuring scalability, robustness, security, and operational excellence., * 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. ## Description * 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/GCP, 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. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Stop using Node.js like in 2020! 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