zohaib z.

🚀8+ years building real-world AI & LLM systems for startups and teams

I help founders and teams turn AI ideas into real, working products — starting from PoCs and MVPs, and evolving them into robust, production-ready systems as the business scales.

I’m an AI Engineer & Architect with 8+ years of hands-on experience, working closely with startups, founders, and product teams to solve real-world problems using modern Generative AI, LLM systems, and cloud-native architectures.
Whether you’re experimenting, validating an MVP, or scaling to production, my focus is on clean architecture, future-proof design, and practical trade-offs — so early AI decisions don’t become bottlenecks later.

🤖 Generative AI & Large Language Model Systems
I design and implement model-agnostic LLM architectures, so products aren’t locked into a single provider.
⚫GPT-5.2, GPT-4o, GPT-4 Mini (OpenAI / Azure OpenAI)
⚫Anthropic (Claude), Google Gemini
⚫Prompt engineering, tool/function calling, structured outputs
⚫Multi-model routing, evaluation & fallback strategies
⚫ Embedding AI deeply into product workflows (not just chat)

📚 RAG, Knowledge Bases & Knowledge Graphs
I build LLM systems that reason over real data, not hallucinations.
⚫End-to-end LLM-RAG architectures (PoC → MVP → scale)
⚫Vector search + semantic search + hybrid retrieval
⚫Recursive & multi-level chunking strategies
⚫Dynamic context assembly & relevance scoring
⚫Knowledge bases & knowledge graphs (Neo4j)
⚫Hierarchical document structures & graph-based retrieval

🤖 AI Agents & Agentic Workflows
I specialize in agentic systems that actually perform work, not just respond to prompts.
⚫Single & multi-agent architectures (planner, executor, critic)
⚫LangChain, LangGraph, Cloud Agent SDK, AutoGen, CrewAI
⚫Agent orchestration, agent protocols & A2A patterns
⚫Tool-using agents for operations, analytics & automation
⚫Stateful agents with memory & human-in-the-loop controls
⚫Event-driven and workflow-based agent system

⚙️ MLOps, LLMOps & Observability
I treat AI like production software, not experiments.
⚫CI/CD for ML & LLM pipelines
⚫Dockerized & Kubernetes-based deployments
⚫MLflow, Vertex AI, Azure Machine Learning
⚫Model versioning, evaluation & rollback strategies ⚫LLM observability with Langfuse ⚫Metrics & monitoring using Prometheus & Grafana ⚫Cost-aware inference & performance optimization