Role: Full Stack Architect AI & Agentic Systems
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, 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, 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)
- 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.
VeeRteq Solutions is an Equal Opportunity Employer
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