Iβm a Machine Learning Engineer with 6+ years of experience shipping ML to production β from MLOps and scalable data/feature pipelines to deep learning models in the wild. I love clean engineering, measurable impact, and continuous learning.
- Operating Systems: Windows, Linux
- Languages: Python, SQL, C#
- ML & Data: TensorFlow, Keras, PyTorch, PyG, scikit-learn, Pandas, NumPy, Matplotlib/Seaborn, NLP/NLTK
- Experimentation: MLflow, DVC
- Packaging & Serving: FastAPI, Flask, BentoML
- Pipelines & Orchestration: TFX, Kubeflow, Jenkins
- Containers & Clusters: Docker, Kubernetes
- IaC & Ops: Ansible, Terraform
- Observability: Elasticsearch, Kibana, Grafana
- VCS: Git
- GCP: Vertex AI, BigQuery
- AWS
- AI-Powered Petition Search β Part 1: FastAPI backend + semantic search (Sentence-Transformers), responsive UI, and analytics for UK Parliament petitions.
- AI-Powered Petition Search β Part 2: Low-code/agentic system with n8n + Supabase hybrid search + GPT orchestration for conversational querying.
- Drug Discovery (MLOps): End-to-end pipeline for ADMET_PAMPA_NCATS β training, registry/feature store on AWS, CI/CD with Jenkins, and monitoring via Prometheus/Grafana.
- TensorFlow Developer Certificate
- TensorFlow: Advanced Techniques
- Advanced Machine Learning on Google Cloud
- Machine Learning Engineering for Production (MLOps)
- DeepLearning.AI TensorFlow Developer
- Preparing for Google Cloud Certification: ML Engineer
- Microsoft Certified: Azure AI Fundamentals
- TensorFlow Developer Certificate in 2023 (ZtM)
- PyTorch for Deep Learning in 2023 (ZtM)
- The Git & GitHub Bootcamp


