class AIEngineer:
def __init__(self):
self.name = "Milind Bhushan"
self.role = "AI Engineer"
self.languages = ["Python", "Java", "JavaScript", "TypeScript", "CUDA"]
self.architecture = ["Transformers", "Diffusion Models", "RAG", "Multi-Agent Systems"]
self.currently_building = "Production ML pipelines & LLM-powered applications"
self.fun_fact = "I debug neural networks for fun"
def __str__(self):
return f"Turning caffeine into intelligent systems since 2020"
me = AIEngineer()
| Domain | Technologies |
|---|---|
| LLM & GenAI | GPT, Claude, LLaMA, Fine-tuning, RLHF, Prompt Engineering |
| ML/DL Frameworks | PyTorch, TensorFlow, JAX, HuggingFace, LangChain |
| MLOps & Infra | Kubernetes, Docker, MLflow, Weights & Biases, Ray |
| Data & Compute | CUDA, PostgreSQL, MongoDB, Pinecone, SQL |
| Cloud & Deploy | Azure ML, AWS SageMaker, GCP Vertex AI, Modal, Replicate |
| ML Tooling | Jupyter, Pandas, NumPy, MLflow, W&B, OpenCV |
🔬 Full Arsenal (click to expand)
Languages & Runtimes
AI/ML Frameworks & Tools
Infrastructure & DevOps
Data & Databases
- 🧪 Fine-tuning open-source LLMs for domain-specific tasks
- 🏗️ Building production-grade RAG systems with evaluation pipelines
- 🤖 Multi-agent orchestration frameworks
- ⚡ Optimizing inference latency with quantization & speculative decoding
- 📝 Writing about ML systems design & best practices
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"Any sufficiently advanced AI is indistinguishable from magic." — Arthur C. Clarke (adapted)