I build production agentic AI systems that connect to where enterprise data actually lives. Currently architecting AI tools at a Fortune 500 company in Munich.
Enterprise MCP Server Collection (Agentic AI Platform) — 9-system integration with 209+ AI tools
- Confluence RAG with pgvector semantic search (60-80% token reduction)
- AI-powered knowledge search (42x faster, 44% relevance improvement)
- Suite tool aggregation (90% context overhead reduction: 15-20k → 2k tokens)
- Full-stack AI assistant: React, Next.js 15, TypeScript, real-time streaming
Autonomous Plugin Engine — AI-driven code generation for enterprise integrations
- Converts natural language → production MCP servers in 60 seconds
- 49 engine tools, 2,836 tests, 6 auth archetypes (SSO, OAuth2, PAT, API key)
- Learned knowledge base: URL patterns, auth signatures, API conventions
- 15 production plugins auto-generated (2-3 days → 2 minutes per integration)
GenAI Platform Features — Core contributor shipping production AI
- Context-aware AI chat suggestions (Azure OpenAI, Python + Angular, 3,000+ lines)
- Agent onboarding UX system (14 files, 61 commits)
- Enterprise document extraction for RAG pipelines
Production RAG Systems — From hackathon prototype to enterprise deployment
- 3rd prize, GenAI Hackathon (50+ teams) — production-ready RAG with Azure OpenAI
- Delta-sync indexing, smart chunking, trust scoring
| What | Impact |
|---|---|
| AI tools built | 209+ across 9 enterprise systems |
| Search speed improvement | 42x faster (7-10s → 0.2-0.5s) |
| Context overhead reduction | 90% (15-20k → 2k tokens) |
| Token reduction (RAG) | 60-80% via semantic chunking |
| Search relevance improvement | 44% with trust scoring |
| Integration dev time | 2-3 days → 2 minutes (plugin engine) |
| CI/CD governance scale | 15+ teams, 100+ developers |
AI & GenAI: Python, MCP SDK, LangChain, RAG, Agentic AI, Agentic Workflows, pgvector, Azure OpenAI, AWS Bedrock, Embeddings, Vector Databases
Frontend: React, Next.js 15, TypeScript, Angular, Tailwind CSS
Backend: Python, Node.js, Express, PostgreSQL, REST APIs
Cloud & Infrastructure: AWS, Azure, Docker, Kubernetes, Terraform, GitHub Actions
DevOps & Security: CI/CD, Jenkins, SonarQube, OWASP, Monitoring
AI-to-Teams messaging via MCP — Adaptive Cards, rate limiting, priority notifications. Open source.
AI experiments and learning projects.
- AWS Certified Cloud Practitioner
- Certified SAFe 5 Practitioner
- ISTQB Certified Tester
I write weekly about building enterprise AI systems on LinkedIn. Recent topics:
- MCP architecture for enterprise AI (209+ tools, 9 systems)
- What agentic AI development looks like in enterprise
- Production RAG: what breaks at scale
- LinkedIn: linkedin.com/in/alaminasif
- Email: [email protected]
- Location: Munich, Germany