.Net Full Stack Architect AI & Agentic Systems

Job Title: .Net Full Stack Architect - AI & Agentic Systems (ReactJS, NextJS, NodeJS)

Location: Chicago, IL(Hybrid- 3 days/week in office)

Employment Type: Contract

Job Description:-

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, TypeScript, 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 LangGraph, Semantic Kernel, AutoGen, 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:

o Prompt Engineering

o Context Engineering

o Evaluation & Benchmarking

o Model Selection

o RAG Validation

o Agent Testing

o AI Security Reviews

o Responsible AI Compliance

Develop AI observability frameworks to monitor:

o Accuracy

o Hallucinations

o Latency

o Token Consumption

o Cost

o User Satisfaction

Implement AI release governance, validation gates, and production readiness assessments.

Cloud, DevOps & 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

TypeScript

JavaScript (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

LangChain / LangGraph

MCP (Model Context Protocol)

AI Guardrails

Prompt Engineering

Context Engineering

AI Evaluation Frameworks

Cloud & DevOps

Azure / AWS

Docker

Kubernetes

Azure DevOps

GitHub Actions

Jenkins

Observability Platforms