Zainab Z.

I design and build AI systems that have to work outside the demo. My work spans agentic AI, LLM applications, RAG and knowledge systems, conversational AI, real-time decision intelligence, and the backend/data infrastructure required to run them reliably in production. I’ve worked professionally in AI/ML since 2018, with a foundation in machine learning, deep learning, NLP, statistics, and model evaluation that predates the current LLM wave. Today, I combine that foundation with modern LLMs, open-weight models, RAG, agents, tool use, and production AI engineering. As a TopTal certified AI architect (top 3% global talent) with 8+ years of experience, I specialize in designing 𝐋𝐋𝐌-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬, 𝐑𝐀𝐆 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬, and 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐚𝐠𝐞𝐧𝐭𝐬 used in fintech, cybersecurity, and enterprise AI decision systems. WHAT I HELP CLIENTS BUILD • Agentic AI systems — tool use, multi-step workflows, state/memory, orchestration, human-in-the-loop • Production LLM applications — structured outputs, evaluation, guardrails, model/provider integration • RAG & knowledge systems — ingestion, chunking, embeddings, vector/hybrid retrieval, reranking, evaluation • Conversational AI — support assistants, enterprise copilots, workflow automation, contextual/multi-turn systems • AI + data platforms — real-time pipelines, decision intelligence, structured/unstructured data processing • ML systems — NLP, time-series, classification, anomaly detection, model training/fine-tuning • AI architecture — APIs, microservices, deployment, observability, security, scalability, CI/CD PRODUCTION EXPERIENCE One of the systems I helped build is CARA (Cyber Aware Response Analyst), an enterprise conversational AI platform for cybersecurity. CARA has operated in production for 5+ years and integrates with 50+ cybersecurity products and services. My work included conversational intelligence, context and memory, intent/entity understanding, knowledge retrieval, orchestration/integration workflows, data processing, and AI system architecture. Interestingly, much of this work predates today's "AI agent" terminology. The underlying problems were already there: maintaining state, selecting capabilities, interacting with external systems, processing results, handling failures, and deciding what should happen next. I currently lead AI and data systems work spanning real-time decision intelligence, distributed data pipelines, agentic workflows, and People/Organization Intelligence systems. HOW I ENGINEER AI I treat the model as one component of a software system — not the system itself. That means thinking about: • What happens when retrieval returns the wrong evidence? • What happens when a tool/API fails? • How do we evaluate whether the system is actually improving? • What state needs to persist between interactions? • Which decisions belong to an LLM, deterministic software, or a human? • How do we control authorization, validation, retries, observability, latency, and cost? • Can we trace a wrong answer back to the layer that caused it? I use AI where semantic reasoning adds value and deterministic engineering where reliability matters. TECHNOLOGY Python • FastAPI • PyTorch • Transformers • Scikit-learn LangChain • LlamaIndex • RAG • Agentic AI • MCP PostgreSQL • MongoDB • Redis • Kafka • Spark FAISS • Pinecone • OpenSearch AWS • GCP • Docker • Kubernetes REST APIs • Microservices • SQL/NoSQL • CI/CD I also work extensively with AI coding tools including Claude Code and Codex. I use them to accelerate research, implementation, testing, debugging, and iteration while retaining engineering ownership of the resulting system. If you’re building an AI product where reliability, architecture, data quality, evaluation, and production behavior matter, not just getting an impressive prototype running, I’d be happy to discuss it.