Building reliable AI systems focused on retrieval, reasoning, orchestration, and real-world decision workflows.

Generative AI Engineer focused on building production-grade, reliable, and observable AI systems for complex real-world environments.

Currently pursuing a Master’s in Artificial Intelligence, with research focused on Multi-Modal GraphRAG, retrieval optimization, and agentic workflows for technical reasoning systems.

• Agentic AI & Multi-Agent Systems
  Agent design, Hierarchical agents, planning, memory architectures, tool calling, and orchestration using LangGraph & PydanticAI

• Advanced RAG & Knowledge Systems
  Hybrid RAG, GraphRAG, Knowledge Graphs (Neo4j), vector retrieval, reranking, and evaluation

• LLM Engineering & Optimization
  Fine-tuning, quantization, guardrails, structured generation, and model routing

• Production AI & LLMOps
  Observability, tracing, monitoring, evaluation pipelines, and scalable deployment workflows

• Multimodal & Autonomous Systems
  Vision-Language Models (VLMs), autonomous AI systems, and world-model exploration

• World Models & Autonomous AI Systems
• Agentic Workflows & Multi-Agent Collaboration
• Multi-Modal GraphRAG Systems
• Scalable LLM Serving
• Retrieval Optimization & Hybrid Search
• Agent Evaluation & Observability

Open to impactful opportunities in Generative AI, Agentic Systems, AI Infrastructure, and Aerospace AI.

• LinkedIn → https://www.linkedin.com/in/-mutlaq/