Azure AI Foundry Β· Microsoft Fabric Β· MCP Β· FastAPI Β· Python
I build grounded enterprise AI systems that connect reliable data, retrieval, orchestration, validation, and decision-ready outputs.
name: Satnam Singh
role: Agentic GenAI Developer Β· Associate Consultant
location: Gurugram, India
experience: 2+ years
focus:
- Agentic AI systems
- Enterprise knowledge layers and MCP
- Python and FastAPI backends
- Microsoft Fabric analytics
- Document intelligence
- Data quality and governed retrieval
open_to:
- Agentic AI roles
- Python backend roles
- Remote and contract opportunities- π Building enterprise-grade systems with Azure AI Foundry, Microsoft Fabric, Azure AI Search, MCP, FastAPI, Function Apps, Logic Apps, Redis, SQL, and PySpark
- π§ Focused on grounded reasoning, reusable orchestration, typed contracts, validation, observability, and secure Azure integration
- π Recognized with a Client Impact Award and Client Impact Champion certificate
- π Microsoft-certified in Fabric Data Engineering
- π Portfolio: satnamsingh.in
- π« Reach me at satnamsjob@gmail.com
| Project | What it demonstrates | Core stack |
|---|---|---|
| Enterprise Agentic Analytics Platform | Multi-agent analytics, KPI planning, SQL generation, Fabric execution, validation, charts, and structured insights | FastAPI, Azure OpenAI, Fabric, Azure AI Search, Redis, SQL |
| PolicyLens Document Intelligence | Traceable extraction, hybrid retrieval, bounded LLM reasoning, deterministic rules, and validation | Python, PyMuPDF, pdfplumber, Groq, Pydantic |
| Enterprise Knowledge Layer & MCP | MCP server design, multi-index retrieval, product routing, Foundry agents, and identity-aware Azure integration | MCP, Azure AI Search, Foundry, Function Apps, Entra ID |
| AI Application Review Agent | Rules-aware review, duplicate checks, grounded recommendations, and workflow orchestration | Azure AI Foundry, Fabric, Azure AI Search, Logic Apps |
| Startup Intelligence & Advisor Platform | Account analysis, portfolio intelligence, readiness assessment, and grounded recommendations | Foundry, Fabric, Azure AI Search, DQM |
| Movie Recommendation Engine | Content-based recommendation, feature engineering, similarity search, NLP inference, and API integration | Python, scikit-learn, Flask, TMDB API, BeautifulSoup |
| Automated Data Quality Framework | Config-driven PySpark validation, schema drift, reconciliation, thresholds, and observability | PySpark, Microsoft Fabric, Delta Lake, DQM |
| Interactive Agentic Portfolio | Full-stack portfolio engineering, CV-grounded AI, Redis analytics, serverless APIs, and production deployment | React, TanStack Start, Vite, Tailwind, Groq, Upstash, Vercel |