$ aboutme const aseem = { role: "AI/ML Engineer", experience: "6+ years", education: ["M.Sc. AI (Germany)", "B.Tech CSE (India)"], footprint: ["Germany", "India"], build_mode: "production-first", motto: "If it doesn’t ship, it's just a Jupyter notebook." }; What I actually do Architecting 'Zero-Touch' probabilistic decision engines. Translating SOTA papers into reproducible production systems. Accelerating training loops via Multi-GPU & Mixed Precision. Building high-throughput Feature Stores for real-time scoring.

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🚀 Tech Stack & Tooling

🧠 AI Agents & GenAI LangChain, LangGraph, Agentic Systems RAG Pipelines, Llama-3, Prompt Engineering Vector DBs: Pinecone, Milvus, Weaviate 🔥 Machine Learning PyTorch, TensorFlow, Distributed Training (DDP) Scikit-learn, XGBoost, LightGBM Hugging Face Transformers

🏗 Backend & Infrastructure Python (Pydantic), FastAPI Docker, Kubernetes, Terraform AWS: EKS, Lambda, S3 📊 Data Engineering Apache Spark, PySpark Event-Driven Architecture, SQL Palantir Foundry

📈 Analytics & Experimentation Tableau, PowerBI, Looker A/B Testing, Hypothesis Testing, Statistical Inference ⚙ Core Competencies System Design MLOps (CI/CD for ML) Algorithm Optimization Technical Leadership

Focus Areas

🧠 Deep Learning architectures that survive deployment distillation / quantization accuracy ↔ latency tradeoffs

🚀 ML Systems inference at scale (online + batch) monitoring, drift, incident-friendly ops reproducible pipelines (CI/CD for ML)

✨ Generative AI agentic workflows + RAG pipelines synthetic data generation (Diffusion / GANs) vector search & LLM orchestration

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