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

Job Description

Position: 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

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

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• Frontend ReactJS NextJS TypeScript JavaScript (ES6+) HTML5/CSS3 Redux / Redux Toolkit Responsive & Accessible UI Design
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• Backend NodeJS ExpressJS .NET Core (.NET 6+ / .NET 8) ASP.NET Core Web API / REST API C#
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• Databases SQL Server PostgreSQL MongoDB Vector Databases (Pinecone, Azure AI Search, Weaviate, Chroma, Milvus)
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• Architecture Microservices API-First Design Event-Driven Architecture DDD CQRS SOLID Principles
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• Design Patterns AI & Agentic AI Azure OpenAI / OpenAI / Anthropic / Gemini RAG Architecture Agentic
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• Workflows Multi-Agent Systems Semantic Kernel LangChain / LangGraph MCP (Model Context Protocol)
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• AI Guardrails Prompt Engineering Context Engineering AI Evaluation Frameworks Cloud & DevOps Azure / AWS Docker Kubernetes
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• Azure DevOps GitHub Actions Jenkins Observability Platforms
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-Thanks & regards

Akram Khan