What are the Tech Stack a GEN AI Engineer must know?

A GenAI (Generative AI) Engineer requires a specialized tech stack that spans model development, fine-tuning, optimization, retrieval-augmented generation (RAG), deployment, and MLOps. Here's a comprehensive tech stack for a Generative AI Engineer:

---

1. Core Programming & Development

• Python – The primary language for AI development
• Rust / C++ – For performance-critical AI workloads
• JavaScript (Node.js) – If integrating AI models with web apps

---

2. LLMs & Generative AI Frameworks

• Hugging Face Transformers – Pretrained models (GPT, LLaMA, Falcon, etc.)
• LangChain / LangGraph – Framework for building LLM-powered workflows
• LLamaIndex – Indexing and RAG for LLM applications
• TensorFlow / PyTorch – Core deep learning frameworks
• DeepSpeed / FlashAttention – Efficient training and inference for LLMs

---

3. Model Fine-Tuning & Optimization

• LoRA / QLoRA – Efficient fine-tuning of large models
• PEFT (Parameter-Efficient Fine-Tuning) – Optimizing memory usage
• FSDP / DeepSpeed-Zero – Model parallelism for scaling LLMs
• vLLM / TGI (Text Generation Inference) – High-throughput inference

---

4. Retrieval-Augmented Generation (RAG)

• FAISS / Annoy / ScaNN – Vector search for similarity-based retrieval
• Pinecone / Weaviate / ChromaDB – Vector databases for LLM memory
• Milvus / Qdrant – Scalable vector indexing solutions
• Elasticsearch / Vespa – Hybrid search combining keyword + embeddings

---

5. Data Handling & Feature Engineering

• Pandas / NumPy – Data manipulation
• HDF5 / Parquet – Optimized storage formats for large datasets
• Apache Spark / Ray / Dask – Distributed data processing
• Hugging Face Datasets – Pre-built datasets for training LLMs

---

6. Model Deployment & Serving

• FastAPI / Flask – API frameworks for serving LLMs
• Triton Inference Server – Scalable AI model inference
• TensorRT / ONNX Runtime – Model optimization for faster inference
• Ray Serve – Scalable LLM serving
• MLflow / Kubeflow – Model tracking and MLOps

---

7. Cloud & Compute Infrastructure

• AWS SageMaker / Bedrock – Managed AI services
• Google Vertex AI / Azure OpenAI – Cloud AI solutions
• Lambda Labs / RunPod / Modal – On-demand GPU compute
• NVIDIA CUDA / ROCm – GPU acceleration for training & inference

---

8. Orchestration & MLOps

• Apache Airflow / Prefect – Workflow automation for LLM pipelines
• Ray / Dask – Distributed computing for AI workloads
• Weights & Biases – Experiment tracking & visualization
• BentoML – Model packaging and serving framework

---

9. Graph & Knowledge Base Systems

• Neo4j – Knowledge graphs for AI reasoning
• GraphDB – Semantic storage for structured AI knowledge
• Wikidata / ConceptNet – External knowledge sources

---

10. Computer Vision & Multimodal AI

• CLIP / BLIP – Image-text models
• Stable Diffusion / Midjourney APIs – Generative image models
• Whisper / Deepgram – Speech-to-text models

---

BONUS: Agentic AI Frameworks

• LangGraph – Graph-based reasoning for LLM workflows
• AutoGPT / BabyAGI – Autonomous AI agent frameworks
• CrewAI – Multi-agent task automation

---

This tech stack enables a Generative AI Engineer to build, fine-tune, optimize, and deploy state-of-the-art AI systems.