I am a software architect and hands-on engineering leader with 17+ years of experience designing, building, modernizing, and supporting enterprise applications, SaaS platforms, cloud-native systems, secure API ecosystems, and AI-enabled business solutions.

My core strength is bringing together enterprise backend engineering, Azure cloud architecture, API security, DevOps automation, frontend delivery, GenAI/RAG, AI agent workflows, and data engineering to build practical, production-ready systems that solve real business problems.

I have worked across roles from developer to technical lead and architect, delivering systems using ASP.NET Core, Node.js, React, Next.js, SQL Server, PostgreSQL, Azure PaaS, Azure DevOps, Microsoft Fabric, Azure AI Foundry, Azure OpenAI, Azure AI Search, Semantic Kernel, LangChain, and LangGraph.

My current focus is on enterprise AI systems: RAG-based knowledge assistants, business-line AI agents, state-machine-driven workflows, secure tool-calling agents, Microsoft Fabric/Lakehouse data engineering, and Azure-native AI application architecture.

• Design and modernize enterprise cloud applications on Microsoft Azure
• Architect multi-tenant SaaS platforms with secure tenant isolation
• Build API-first backend systems using ASP.NET Core, Node.js, Express, and Python/FastAPI
• Develop React / Next.js frontend applications for enterprise and SaaS products
• Design secure API ecosystems with OAuth2, OpenID Connect, JWT, RBAC, Entra ID, and identity integration
• Implement CI/CD pipelines, DevOps automation, release workflows, and production monitoring
• Build GenAI/RAG applications using Azure AI Foundry, Azure OpenAI, embeddings, Azure AI Search, and vector search
• Design AI agent workflows for business-line automation using Semantic Kernel, LangChain, and LangGraph
• Build state-machine-based AI workflows for predictable, auditable, multi-step business processes
• Work on Microsoft Fabric, Lakehouse, Data Lake, PySpark, and reporting workflows
• Support architecture reviews, cloud cost optimization, production stabilization, troubleshooting, and technical consulting

I am currently focused on building and strengthening enterprise-grade capability in:

• GenAI / RAG applications for enterprise knowledge search and assisted decision-making
• AI agent creation for sales, support, operations, reporting, internal workflow automation, and business-line productivity
• State-machine-driven agent workflows where each step has clear input, output, validation, retry, escalation, and approval handling
• Tool-calling agents that can interact with APIs, databases, documents, knowledge bases, and enterprise systems
• Semantic Kernel, LangChain, and LangGraph for agent orchestration, planner-executor workflows, tool routing, memory, and controlled execution
• Human-in-the-loop patterns for sensitive actions such as email generation, ticket updates, reporting, approvals, and business workflow execution
• Azure AI Foundry, Azure OpenAI, Azure AI Search, embeddings, vector search, and RAG architecture
• Python FastAPI for AI orchestration services and backend AI microservices
• Microsoft Fabric, Data Lake, Lakehouse, Bronze/Silver/Gold layers, and PySpark-based transformations
• Secure cloud-native architecture for AI-enabled enterprise systems

• Azure PaaS
• Azure App Service
• Azure Functions
• Azure SQL
• Azure Storage
• Azure Service Bus
• Azure Key Vault
• Azure Monitor / Application Insights
• Azure DevOps
• Pulumi / Infrastructure as Code
• Multi-tenant SaaS architecture
• API-first architecture
• Microservices and event-driven systems
• Cloud cost optimization
• Dev, UAT, and Production environment design
• Production support and platform stabilization

• Azure AI Foundry
• Azure OpenAI
• GPT-4o / GPT-4o-mini
• Retrieval-Augmented Generation
• Embeddings
• Azure AI Search
• Vector Search
• Prompt Engineering
• Semantic Kernel
• LangChain
• LangGraph
• AI agent orchestration
• Business-line AI agents
• Tool-calling workflows
• Planner-executor patterns
• State-machine-based AI workflows
• Human-in-the-loop approval
• Agent memory and workflow state management
• RAG-powered agents
• Private / on-prem AI architecture
• Llama model workflows
• QLoRA tuning workflows
• Audio/video-to-text data preparation
• JSONL dataset preparation

• ASP.NET Core
• C#
• Web API
• Entity Framework
• Node.js
• Express.js
• Python / FastAPI
• REST APIs
• GraphQL
• OAuth2 / OIDC
• JWT
• API Security
• RBAC
• Clean Architecture
• CQRS
• Domain-Driven Design
• Backend-for-Frontend
• Integration architecture
• Secure backend platform design

• React.js
• Next.js
• JavaScript / TypeScript
• React Native
• HTML / CSS / SASS
• Component-based UI development
• Enterprise dashboard and admin UI development
• API integration and frontend architecture
• SaaS product UI development

• SQL Server
• Azure SQL
• PostgreSQL
• MongoDB
• Cosmos DB
• Microsoft Fabric
• Data Lake / Lakehouse
• Bronze / Silver / Gold data layers
• PySpark transformations
• Reporting data models
• BOM reporting
• Query optimization
• Data ingestion and transformation workflows

• Azure DevOps
• GitHub Actions
• CI/CD pipelines
• Docker
• Kubernetes / AKS
• Azure Container Registry
• Release automation
• Environment configuration
• Monitoring and logging
• Production support
• Secure deployment practices
• Infrastructure troubleshooting

Building an enterprise-style GenAI application using Next.js, Node.js, Express.js, Azure AI Foundry, Azure OpenAI, Azure AI Search, embeddings, PostgreSQL, RAG, and vector search.

Key areas:

• LLM-based chat interaction using Azure AI model deployment
• Chat history storage in PostgreSQL
• Embedding generation and Azure AI Search integration
• RAG flow for grounded enterprise knowledge answers
• Source-aware retrieval and response generation
• Modular architecture for Python FastAPI-based AI orchestration
• Roadmap toward business-line AI agents, tool calling, and workflow automation

Designing agentic AI workflows for business processes where AI systems do more than answer questions — they retrieve context, reason through tasks, call tools, follow workflow states, and support controlled execution.

Key areas:

• Business-line AI agents for sales, support, operations, reporting, and internal automation
• State-machine-based workflow orchestration for predictable multi-step execution
• Semantic Kernel, LangChain, and LangGraph-based agent design
• Tool-calling agents for APIs, databases, documents, and knowledge systems
• RAG-powered agents that retrieve trusted enterprise context before responding or acting
• Human approval before sensitive actions such as sending emails, updating records, generating reports, or triggering workflow steps
• Audit logging, validation, retry handling, guardrails, and workflow recovery
• Secure, observable, cost-aware agent architecture for enterprise adoption

Worked on a Microsoft Fabric-based data engineering and reporting workstream involving SQL Server ingestion, Lakehouse, Data Lake, PySpark, Bronze/Silver/Gold layers, and BOM reporting.

Key areas:

• Data movement from SQL Server source systems
• Lakehouse-based data organization
• Bronze, Silver, and Gold layer design
• PySpark transformation workflow
• Reporting-focused data preparation
• BOM explosion reporting logic and hierarchical data understanding

Contributed to a private AI infrastructure POC focused on open-source LLMs, Llama models, LangChain, QLoRA workflows, Azure ML Workspace, audio/video-to-text processing, JSONL data preparation, and private AI deployment patterns.

Key areas:

• Private/on-prem AI architecture
• Open-source LLM workflow understanding
• Audio/video transcription to text-based datasets
• Dataset preparation for fine-tuning or knowledge ingestion
• Azure ML Workspace-based experimentation flow
• Data privacy and offline AI deployment considerations

Worked on an Azure-hosted Kentico Xperience 13 platform for a global mining technology customer. The platform supported CMS Admin, public marketing website, contact forms, content workflows, and business-facing web content.

Key areas:

• Dev, UAT, and Production environment understanding
• Azure App Service, Azure SQL, Azure Storage, and DevOps pipeline support
• Pulumi Infrastructure as Code deployment flow
• CMS Admin and public site support
• Hotfix / upgrade planning
• Production support transition after previous vendor exit

Architected and supported a SaaS-based e-commerce platform for a US-based dental equipment supplier using ASP.NET Core, React, Azure App Service, Azure Functions, Azure SQL, and Azure DevOps.

Key areas:

• Multi-tenant architecture
• React frontend and ASP.NET Core API backend
• Azure DevOps CI/CD pipelines
• Supplier and clinic workflows
• Production support and cloud cost optimization

Good architecture is not only about drawing boxes.

For me, architecture means:

• Understanding the business problem clearly
• Choosing simple, maintainable technical patterns
• Designing for security, scale, cost, and operations
• Keeping delivery realistic for the team
• Avoiding unnecessary complexity
• Making systems easier to support after go-live
• Building systems that can evolve without becoming fragile
• Designing AI workflows that are useful, auditable, secure, and controlled

I prefer practical architecture that teams can actually build, deploy, troubleshoot, operate, and improve.

• Email: as.abhishek@gmail.com
• Stack Overflow: Abhishek Singh
• LinkedIn: Abhishek Singh