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

Talentola; Chicago (IL); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Talentola is seeking a highly experienced Full Stack Architect AI & Agentic Systems to lead design and implementation of next-generation platforms powered by modern web technologies and AI-driven architectures. You will integrate frontend and backend ecosystems, align with enterprise IT standards, and guide AI workflows from concept through production.

The role covers enterprise architecture, agentic AI solutions, RAG, AI‑DLC, cloud-native delivery on Azure/AWS, and MLOps.

Qualifications

• Front-end and back-end stack expertise with modern web technologies.
• Experience building AI-enabled, enterprise-grade platforms.

Responsibilities

• Define end-to-end architecture for enterprise and AI-enabled platforms.
• Architect modern frontend and backend services with microservices and API-first design.
• Design AI agentic workflows and RAG-based solutions.
• Lead AI Development Lifecycle governance, testing, security, and compliance.
• Mentor architects, engineers, and AI engineers.

Skills

ReactJS

NextJS

TypeScript

JavaScript

HTML/CSS

Redux Toolkit

Responsive UI

NodeJS

ExpressJS

.NET Core

Web API

C#

SQL Server

PostgreSQL

MongoDB

Vector DBs

Azure

AWS

Docker

Kubernetes

CI/CD

AI Governance

LangGraph

Semantic Kernel

RAG

Agentic AI

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, 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:
  • 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, 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

Job Location: Chicago, IL(Hybrid- 3 days/week- WFO)

Duration: 12 Months

Preferred Qualifications

• Experience delivering AI‑powered healthcare, payer, provider, or life sciences solutions.
• Experience with Healthcare interoperability standards (FHIR, HL7).
• AI Governance and Responsible AI experience.
• Exposure to AI‑driven SDLC transformation and engineering productivity platforms.
• Experience implementing enterprise‑scale Copilot or Agentic AI ecosystems.

Ideal Candidate Profile

A strategic technology leader who can seamlessly bridge Full Stack Engineering, Enterprise Architecture, Agentic AI, and AI‑DLC governance, while driving AI‑first transformation initiatives and building secure, scalable, production‑grade intelligent systems that deliver measurable business value.