I'm a ** AI Research and Data Scientist** with over 9 years of experience at the intersection of software engineering, healthcare, and artificial intelligence. I hold a Ph.D. in Informatics from the University of Iowa, where my research focused on applying Natural Language Processing (NLP) and Large Language Models (LLMs) to enhance clinical decision support and symptom detection.

Currently at the FAIR DATA INNOVATION HUB, I design and deploy end-to-end AI solutions for biomedical research — from LLMOps pipelines and data engineering to scalable, cloud-native deployments. My expertise spans Generative AI, Clinical NLP, MLOps/LLMOps, and AI-driven application development.

🔍 I'm passionate about transforming research into real-world impact through AI-powered tools that advance healthcare, science, and evidence-based decision-making.

• 🧪 Data Science & ML Frameworks:
  Python, Pandas, NumPy, Scikit-learn, XGBoost, Keras, TensorFlow, PyTorch, Matplotlib, Seaborn

• ⚙️ MLOps & LLMOps:
  MLflow, DVC, GitHub Actions, Jenkins, Docker, Kubernetes, LangChain, LangGraph, LangSmith, DAGsHub, Airflow, Grafana

• 💬 NLP & LLMs:
  Tokenization, NER, Sentiment Analysis, spaCy, NLTK, Transformers (Huggingface), BERT, GPT, LLaMA, RoBERTa, OpenAI API, Gemini, Ollama

• ☁️ Cloud & Deployment:
  AWS (SageMaker, Lambda, Bedrock, S3), GCP, Minikube, HPC, FastAPI, Flask

• 🛢 Databases & Embedding Stores:
  MS SQL Server, PostgreSQL, Cassandra, FAISS, ChromaDB, ASTAR DB

• 📊 BI & Statistical Tools:
  R, SPSS, SAS, STATA, Power BI

🚀 Follow along as I explore the world of MLOps, LLMOps, and Agentic AI
This series documents my hands-on learning journey through real-world projects, tutorials, and best practices in deploying intelligent systems.

Stay tuned for structured guides, code notebooks, and insights!

• 💼 LinkedIn
• 🌐 DagsHub Profile
• 📚 Google Scholar

“Strive not to be a success, but rather to be of value.” — Albert Einstein