I’m a Machine Learning & AI Engineer focused on building, deploying, and scaling practical AI systems that solve real-world problems.

I’m interested in the engineering layer that transforms AI models from experiments into reliable, reproducible, scalable, and production-ready systems.

My work spans the complete AI lifecycle—from data preparation and model development to API integration, containerization, deployment, monitoring, and intelligent automation.

• 🤖 Generative AI & Agentic Systems
• 🔎 RAG & Knowledge-Based AI Applications
• 🧠 Machine Learning & Deep Learning Systems
• 👁️ Computer Vision Applications
• 💬 NLP & Intelligent Automation
• ⚙️ End-to-End MLOps Pipelines
• 🚀 Production AI APIs & Model Serving
• ☁️ Cloud-Native AI Infrastructure

Building intelligent healthcare systems that combine machine learning with domain knowledge from my background in physiotherapy.

Areas of interest:

• AI-assisted movement and gait analysis
• Healthcare conversational AI
• Clinical decision-support systems
• Patient-facing AI assistants
• Physiotherapy and rehabilitation technology

Developing AI applications using modern LLM architectures and agentic workflows.

Focus areas:

• Retrieval-Augmented Generation (RAG)
• Tool-using AI agents
• Multi-agent workflows
• LangChain & LangGraph
• LLM APIs
• Structured AI workflows
• AI automation

I’m particularly interested in taking ML systems beyond notebooks.

Typical architecture:

Data
  ↓
Data Validation
  ↓
Feature Engineering
  ↓
Model Training
  ↓
Experiment Tracking
  ↓
Model Evaluation
  ↓
API / Model Serving
  ↓
Docker
  ↓
Cloud / Kubernetes
  ↓
Monitoring & Continuous Improvement

A model is only as good as the system supporting it.

A high-performing model is not enough.

I care about:

• Reproducibility
• Scalability
• Reliability
• Observability
• Security
• Maintainability
• Deployment
• Automation
• Real-world business impact

My goal is to bridge the gap between AI research and production engineering.

I’m particularly interested in applying AI to:

🏥 Healthcare & Digital Health 💳 FinTech & Banking 🌍 Climate & Sustainability 🏢 Enterprise Automation 🤖 Intelligent Agents 📊 Data-Driven Decision Systems

• Advanced MLOps & LLMOps
• Kubernetes & Cloud-Native AI
• Production-grade AI architectures
• Agentic AI systems
• Scalable model serving
• AI workflow automation
• End-to-end ML infrastructure

I’m building toward roles where I can work across the intersection of:

Machine Learning + AI Engineering + MLOps + Cloud Infrastructure

I’m open to opportunities as a:

• Machine Learning Engineer
• AI Engineer
• Applied AI Engineer
• MLOps Engineer
• LLMOps Engineer
• ML-focused Data Scientist