- Pursuing a Master’s in Computer Science (AI/ML specialization) at Blekinge Institute of Technology (Sweden).
- Passionate about building intelligent systems and deploying them securely and reliably in the cloud.
- Experience across LLMs, RAG, embeddings, and time-series forecasting, combined with DevOps practices like containerization, CI/CD, and cloud-native deployments.
- Goal: Become a hybrid AI/ML + Cloud/MLOps Engineer — designing models and making them production-ready.
| Category | Tools & Tech |
|---|---|
| AI/ML & LLMs | Python • PyTorch • TensorFlow • HuggingFace • LangChain • Chroma • FAISS |
| DevOps & Cloud | Docker • Kubernetes • GitHub Actions • Linux (cron, shell scripting) • CI/CD |
| Data & Visualization | Pandas • NumPy • SQL • PostgreSQL • Power BI • Matplotlib • Seaborn |
| Other | REST APIs • FastAPI • Anaconda • OpenCV • YOLO • RNN/LSTM/GRU/GANs |
- RAG-PDF-Chatbot – Conversational QA over PDFs with FastAPI, ChromaDB, and Sentence-Transformers.
- Vector-Search-Demo – Minimal FAISS + embeddings CLI for document similarity search.
- Agentic-Research-Assistant – LangChain-based agent that reads local notes, calls tools, and refines outputs.
- GRU-GAN for SOH Estimation – Battery health forecasting using NASA datasets, boosting accuracy by 17%.
- EU Energy Time-Series Forecasting – Forecasting negative electricity prices with LSTM/XGBoost/SARIMA.
- Unified Object Detection – YOLOv8 & Faster R-CNN for industrial safety monitoring.
- AI Deployment Pipelines – Containerized ML apps with Docker + FastAPI, deployable on AWS/GCP.
- CI/CD Workflows – Automated testing and deployment pipelines via GitHub Actions. (work in progress)
- Kubernetes Experiments – Running ML microservices on Minikube and exploring Helm charts. (work in progress)
- MLOps: automated retraining pipelines, monitoring, and drift detection.
- Cloud-native AI: deploying LLM apps with serverless + scalable vector search.
- Agentic workflows: chaining reasoning steps and feedback loops in production settings.