Staff Engineering Manager at Beckman Coulter Diagnostics (Danaher) with 18+ years building enterprise .NET systems in regulated healthcare environments.
I operate at the intersection of engineering, product, and people — leading cross-functional Scrum teams, driving 3-year product roadmaps, and shipping FDA-cleared software while growing engineers who build it.
💡 My Philosophy:
"Knowledge means nothing without hands-on experience" — every engineer should keep learning by building and trying POCs with new tech.
🎯 What Drives Me: Proven track record of building, innovating, and solving real industry problems with confidence. Passionate about transforming knowledge into hands-on expertise and mentoring the next generation of engineers.
- Leading two 9-member Scrum teams (Development + Verification engineers)
- Delivered MVP & MVP+ product features aligned with strategic goals for 3-year product roadmaps
- End-to-end ownership of software product development and delivery
- Working closely with business heads to define and implement strategies for timely, high-quality deliverables
| Layer | Technologies |
|---|---|
| Backend | C# · .NET Core · ASP.NET · WPF · Microservices |
| Frontend | ASP.NET Core MVC · JavaScript · Angular · React · TypeScript · Vite |
| Cloud/DevOps | Azure DevOps · Docker · Kubernetes · GitHub Actions |
| Database & Tools | SQL Server · PostgreSQL · pgvector · Visual Studio · VS Code · UML · C4 Modelling |
| Observability | ELK Stack · Logstash · Grafana |
| AI/ML | Azure OpenAI · GenAI · RAG · GitHub Copilot · Claude · Python · LangChain · LangGraph |
| Architecture | RESTful APIs · Distributed Systems · Microservices · Cyber Security Threat Modelling |
- US Medical Devices — FDA 510(k) certified software
- Regulatory Compliance — 21 CFR 820, ISO 13485
- Healthcare Diagnostics — Beckman Coulter product suite
- US EHR/EMR — Electronic Health & Medical Records systems
- US Life Sciences
- Casino Gaming applications
- US Property & Auto Insurance (INS 21, 22) products
| Dimension | How I Lead |
|---|---|
| 👥 People | Manage & lead 2 Scrum team with 18 engineers across Dev + Verification teams |
| 🎯 Product | Own backlog, roadmap alignment, and MVP delivery with business stakeholders |
| 🏗️ Architecture | Drive code, design & architecture reviews, ADRs, design candidate discussions, and technical strategy |
| 🔄 Cross-functional | Bridge Engineering, Systems, QA, Regulatory, and Business units |
| 📈 Growth | Recruitment, onboarding, appraisals, and capability building |
Turning scattered product knowledge into instant, intelligent answers for engineering teams
A hands-on POC exploring Retrieval-Augmented Generation (RAG) for real-time semantic search across Medical Devices domain knowledge — built for Engineering Managers, Product Owners, and Development Teams.
🎯 Problem Solved: Product and engineering teams spent significant time hunting across siloed sources — IFUs, Design Docs, multi-discipline domain knowledge, and Azure DevOps work items — to answer product and technical questions. This tool unifies that knowledge into a single intelligent search interface.
⚙️ Tech Stack:
| Layer | Technology |
|---|---|
| 🧠 AI / Embeddings | Company-hosted Azure OpenAI endpoint (secured within internal domain) |
| 🗄️ Vector Store | PostgreSQL + pgvector — stores and retrieves document embeddings |
| 🖥️ Backend | Custom .NET Core Web APIs |
| 🎨 Frontend | React + TypeScript + Vite |
| 📦 Data Sources | Product IFUs (PDF) · Design Docs (Word) · Azure DevOps Requirements & Work Items |
🔧 How It Works:
- 📄 Ingest — PDF and Word documents (IFUs, Design Docs) are chunked into segments
- 🔢 Embed — Each chunk is converted to vector embeddings via the internal Azure OpenAI endpoint
- 🗄️ Store — Embeddings stored in PostgreSQL pgvector database
- 🔍 Search — User query is embedded and matched against stored vectors for semantically relevant results
- 💬 Answer — Relevant chunks passed to Azure OpenAI to generate a contextual, grounded response
🔒 Security: All AI inference is routed through an internally hosted Azure OpenAI endpoint — no data leaves company domain, which is critical for regulated Medical Devices environments.
👥 End Users: Engineering Managers · Product Owners · Developers · Cross-functional Technical Teams
🚀 Next Steps:
- Automate full ingestion pipeline using a RAG orchestration framework (LangChain / LangGraph)
- Scheduled sync for Azure DevOps work items and document updates
- Role-based access control and response filtering
📌 Status: POC Complete — Manual ingestion pipeline · Exploring automation & productionization path
- ☁️ Azure & Azure AI/ML — deepening cloud-native AI architecture patterns
- 🤖 Agentic AI — exploring LangChain, LangGraph, CrewAI, Azure AI Foundry for workflow automation
- 🏥 LLM integration patterns for regulated healthcare environments
- 🚀 Next: Automating RAG ingestion pipeline with an orchestration framework
| Certification | Issuer |
|---|---|
| 📘 Azure Fundamentals (AZ-900) | Microsoft |
| ☁️ AWS Cloud Practitioner | Amazon Web Services |
| 🤖 Azure AI Fundamentals (AI-900) | Microsoft |
| 🐍 Python for GenAI | Online |
| 🗄️ SQL Server | Microsoft |
+ 18+ years of proven track record in .NET enterprise application development
+ 🚀 Delivered MVP + MVP+ features for a 3-year strategic product roadmap at Beckman Coulter
+ 👥 Lead & mentor 18 engineers across 2 Scrum teams (Dev + Verification)
+ 🏥 Shipped FDA 510(k)-cleared software under 21 CFR 820, ISO 13485 & IEC 62304
+ ⚙️ Architected distributed systems serving Desktop, Mobile & Web clients
+ 🤝 Cross-functional leader bridging Engineering, Regulatory, QA & Business for on-time delivery
+ 📈 Drove recruitment and capability growth across two cross-functional Scrum teams
+ 🔬 Built RAG-based Knowledge Assistant POC using Azure OpenAI + pgvector + .NET Core + React
