Full Stack Architect - AI & Agentic Systems

About the role

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, and Responsible AI Compliance.
• Develop AI observability frameworks to monitor Accuracy, Hallucinations, Latency, Token Consumption, Cost, and 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

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.