Daniel Gil is a Data Science Manager based in Melbourne with over a decade of experience building AI and ML products across the US, Europe and Australia. He combines hands-on expertise in generative AI, LLM agents, multimodal NLP and computer vision with production-grade MLOps—designing SageMaker pipelines, IaC with Terraform, Airflow/Dagster orchestration and GPU-optimised PyTorch models. His recent work includes scaling RAG for real-estate listings, multimodal recommender systems and image-ranking models that measurably improved engagement and personalization. Daniel moves fluidly between strategic leadership—mentoring teams and shaping data practices—and deep engineering, having led end-to-end data pipelines, model governance and deployment at scale. He also brings a software-architect background and product-minded experience from earlier roles, which helps him translate business problems into robust technical solutions.
8 years of coding experience
20 years of employment as a software developer
Scrum Master Certified, Information Technology Project Management, Scrum Master Certified, Information Technology Project Management at Scrum Agile Institute
Graduate Diploma in Software Management and Engineering, Project Management, software engineering, architecture and design, Graduate Diploma in Software Management and Engineering, Project Management, software engineering, architecture and design at Universidad Católica de Oriente
Big Data Scientist Certified, Data Science, Big Data Scientist Certified, Data Science at Arcitura - Big Data Science School
The University of Melbourne
Project Management Professional (PMP /PMI) Course Prep Exam, Information Technology, Project Management Professional (PMP /PMI) Course Prep Exam, Information Technology at IT Service
Computer Software Engineering, Computer Software Engineering at Carnegie Mellon University
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