class MuhammadAli:
    """Senior Gen AI Engineer & Python Backend Specialist
    3+ years Django/FastAPI backends → 2 years Agentic AI architecture"""

    title = "Senior Gen AI Engineer | Python Backend Specialist"
    location = "Lahore, Pakistan"
    experience = "5+ years total · 2 years Agentic AI · 3+ years Backend Engineering"

    companies = ["Kavak (Contractor)", "Cloudpacer", "Ginkgo Retails"]

    @staticmethod
    def agentic_ai_systems():
        return {
            "architectures": [
                "LangGraph Supervisor Patterns",
                "Multi-Agent Orchestration",
                "Policy-Driven Routing",
                "Dynamic Context Synthesis",
                "Human-in-the-Loop Escalation"
            ],
            "frameworks": ["LangChain", "LangGraph", "CrewAI", "AutoGen", "PydanticAI"],
            "production_scale": "1,000+ daily interactions at 99.5% uptime"
        }

    @staticmethod
    def rag_pipelines():
        return {
            "techniques": [
                "Advanced RAG Architectures",
                "Semantic Search & Retrieval",
                "Context Window Optimization",
                "Vector Database Design"
            ],
            "vector_stores": ["Pinecone", "Chroma", "FAISS", "Qdrant", "Weaviate", "Milvus"],
            "embeddings": ["OpenAI", "Sentence-Transformers", "Cohere"]
        }

    @staticmethod
    def llm_engineering():
        return {
            "models": ["OpenAI GPT-4", "Claude", "Gemini", "Llama", "Mistral"],
            "platforms": ["OpenAI API", "Anthropic API", "GROQ", "Together AI"],
            "techniques": [
                "Prompt Engineering",
                "Chain-of-Thought",
                "Few-Shot Learning",
                "Fine-tuning (LoRA, SFT, PEFT)",
                "Tool Calling & Function Orchestration"
            ]
        }

    @staticmethod
    def backend_architecture():
        return {
            "frameworks": ["FastAPI", "Django", "Flask"],
            "databases": ["PostgreSQL", "MySQL", "Redis", "MongoDB"],
            "messaging": ["RabbitMQ", "Kafka", "Celery"],
            "patterns": [
                "Microservices Architecture",
                "Event-Driven Systems",
                "RESTful APIs",
                "WebSockets",
                "gRPC"
            ],
            "scale": "10,000+ transactions/day for 150+ enterprise clients"
        }

    @staticmethod
    def research_and_ml():
        return {
            "fellowship": "DAAD Research Fellow — RPTU Kaiserslautern-Landau, Germany",
            "computer_vision": ["CNNs", "GANs", "Object Detection", "Multispectral Imaging"],
            "frameworks": ["TensorFlow", "PyTorch", "Scikit-learn"],
            "accuracy": "89% pest detection · 92% flood mapping"
        }