Daniel Manzano
I am a Data Science & Machine Learning Engineer with 10+ years of experience across data engineering, advanced analytics, and applied AI in both industrial and research environments.
About
I am a Data Science & Machine Learning Engineer with 10+ years of experience across data engineering, advanced analytics, and applied AI in both industrial and research environments. I design, build, and operate scalable AWS-based data and ML platforms, delivering end-to-end solutions from data ingestion and transformation to production machine learning systems.
I have hands-on experience deploying ML, NLP, Computer Vision and LLM-based solutions into web and mobile applications. I have a deep understanding of MLOps practices, including model lifecycle management, monitoring, and continuous optimization in production. I enjoy collaborating with cross-functional teams and translating complex technical solutions into tangible business value.
- 8 years' experience
- Joined February 2026
Industry experience
- Computers & Electronics
- Software & Internet
- Healthcare
- Professional Services
- Real Estate & Construction
Work experience· 4 roles
Designed and deployed scalable data and machine learning pipelines for real-estate analytics and forecasting, migrating fully from manual, local model training to 100% automated cloud-native workflows on AWS. End-to-end automation across ingestion, training, scoring, and monitoring using AWS Glue, Lambda, Step Functions, EventBridge, and SageMaker. Built production-grade Generative AI systems using Large Language Models (LLMs) to analyse thousands of customer conversations, automatically generating structured insights, trends, and recommendations. Architected agentic LLM workflows with LangGraph, modelling multi-step reasoning as stateful graphs with validation, retries, and conditional execution, significantly improving output reliability and schema correctness for downstream analytics and dashboards. This work replaced manual, qualitative analysis with fully automated LLM pipelines, reducing time-to-insight by ~80%. Developed and deployed interactive insight applications on streamlit
Delivered production-quality computer vision components using optical flow architectures. Contributed to deployment pipelines using Docker, CI workflows, and real-time API inference. Participated in PR reviews, engineering discussions, and architecture decisions.
Developed deep learning models for healthcare-related motion and activity analysis. Published peer-reviewed work on AI for fall-risk identification in older adults. Created reproducible pipelines for preprocessing, training, and evaluation.
Education
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