Full Stack Architect – AI & Agentic Systems (ReactJS, NextJS, NodeJS

Location: Chicago, IL (Hybrid) Employment:

Contract Job Description: We are seeking a highly experienced Full Stack Architect – AI & Agentic Systems to lead the design and implementation of next-generation digital platforms powered by modern web technologies and AI-driven architectures. The ideal candidate will possess deep expertise in ReactJS, NextJS, NodeJS,.NET Core, ASP.NET Web APIs, cloud-native application development, and enterprise architecture, along with hands-on experience designing and implementing Agentic AI solutions, Retrieval-Augmented Generation (RAG), AI orchestration frameworks, and AI Development Lifecycle (AI-DLC) practices. This role will drive the convergence of traditional software engineering and AI engineering, enabling scalable, secure, and production-ready AI-powered applications.

Key Responsibilities: Enterprise & Solution Architecture Define end-to-end architecture for enterprise applications and AI-enabled platforms. Design scalable systems leveraging microservices, API-first architecture, event-driven patterns, and cloud-native principles. Establish architecture governance, design standards, and engineering best practices. Conduct architecture reviews and technology assessments. Full Stack Architecture Architect modern frontend applications using ReactJS, NextJS, Type Script, and component-driven design. Design backend services using NodeJS,.NET Core, ASP.NET Web APIs, and microservices. Define secure integration patterns across enterprise applications, cloud services, and AI platforms. Drive performance optimization, observability, security, scalability, and maintainability. Agentic AI Solution Architecture Architect autonomous and semi-autonomous AI agents for business process automation. Design multi-agent systems using orchestration frameworks such as Lang Graph, Semantic Kernel, Auto Gen, CrewAI, or similar technologies. Define AI workflows involving planning, reasoning, memory management, tool usage, and human-in-the-loop controls. Architect enterprise-grade RAG solutions integrating vector databases, enterprise knowledge sources, and LLMs. Implement guardrails, AI governance, responsible AI controls, and evaluation frameworks. AI Development Lifecycle (AI-DLC) Establish and operationalize AI-DLC processes across ideation, experimentation, development, deployment, monitoring, and continuous optimization. Define standards for: Prompt Engineering Context Engineering Evaluation & Benchmarking Model Selection RAG Validation Agent Testing AI Security Reviews Responsible AI Compliance Develop AI observability frameworks to monitor: Accuracy Hallucinations Latency Token Consumption Cost User Satisfaction Implement AI release governance, validation gates, and production readiness assessments. Cloud, Dev Ops & MLOps Architect solutions on Azure and/or AWS.Design CI/CD pipelines supporting both software and AI workloads. Integrate AI testing, prompt validation, and model evaluation into engineering workflows. Establish MLOps/LLMOps practices for enterprise deployments. Drive containerization and orchestration using Docker and Kubernetes. Technical Leadership Mentor architects, engineering leads, and AI engineers. Drive AI-first engineering transformation initiatives. Collaborate with business stakeholders to identify and prioritize AI opportunities. Support solutioning, estimations, proposals, and executive presentations.

Required Technical Skills: Frontend ReactJS NextJS Type Script Java Script (ES6+) HTML5/CSS3 Redux / Redux Toolkit Responsive & Accessible UI Design Backend NodeJS ExpressJS.NET Core (.NET 6+ /.NET 8) ASP.NET Core Web API / REST API C#Databases SQL Server PostgreSQL MongoDB Vector Databases (Pinecone, Azure AI Search, Weaviate, Chroma, Milvus)Architecture Microservices API-First Design Event-Driven Architecture DDD CQRS SOLID Principles Design Patterns AI & Agentic AI Azure OpenAI / OpenAI / Anthropic / Gemini RAG Architecture Agentic Workflows Multi-Agent Systems Semantic Kernel Lang Chain / Lang Graph MCP (Model Context Protocol)AI Guardrails Prompt Engineering Context Engineering AI Evaluation Frameworks Cloud & Dev Ops Azure / AWS Docker Kubernetes Azure Dev Ops Git Hub Actions Jenkins Observability Platforms-Thanks & regards Akram Khan