Machine Learning Engineer & Production ML Systems | Backend Platforms

Daniel Hernandez

Machine Learning Engineer building production-grade ML systems across computer vision, LLM-based architectures, and financial infrastructure.

Bogota, Colombia
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About

Daniel Hernandez

Machine Learning Engineer building production-grade ML systems across computer vision, LLM-based architectures, and financial infrastructure. Experienced in designing end-to-end pipelines from feature engineering and model training to scalable deployment, observability, and system optimization.

Strong background in cloud infrastructure, CI/CD, and real-time data systems. Previously interned at Tesla, Inc.

Python SQL C++ PyTorch XGBoost Computer Vision RAG Model Evaluation FastAPI GCP Datadog APM Metrics & Monitoring CI/CD Docker Kubernetes ETL LLM Systems

Experience

Software Engineer (Payments Platform)
PayJoy
Aug 2025 - Present
Bogota, Colombia · Hybrid
  • Optimize payment service database queries and indexing across 2 critical retrieval flows, reducing installment latency and improving backend efficiency under production load
  • Expand observability with 3 telemetry layers (Datadog APM, custom metrics, and alerting), accelerating incident detection and reducing downtime
  • Orchestrate rollout of offline code-generation workflows across 3 functions (engineering, product, and operations), mitigating regulatory risk in Brazil and strengthening operational resilience
  • Drive integration of a new payment processor (Bitso), expected to reduce annual transaction costs by ~$1M while improving payment reliability and scalability
Senior AI & Backend Engineer
Reshut Tech
Dec 2024 - May 2025
Miami, USA · Remote
  • Led end-to-end computer vision development across 2 production model families (YOLOv7 and Detectron2), improving model quality through systematic fine-tuning and evaluation
  • Architected ML deployment workflows on Google Cloud Platform with 3 delivery pillars (containerization, CI/CD, and service scaling), enabling repeatable production releases
  • Introduced production monitoring and logging for AI services with metric-based alerting, improving runtime reliability and reducing issue triage time
  • Partnered with leadership on quarterly ML milestones and delivery plans in an agile workflow, keeping execution aligned with product goals
Data Scientist
Dataplicada
Aug 2020 - Nov 2024
Miami, USA · Remote
  • Designed and deployed a Retrieval-Augmented Generation (RAG) system with LangChain and 2 LLMs (Llama3 and GPT-4), turning large-scale customer feedback into actionable insights
  • Created Python ETL pipelines for 2 data modalities (structured and unstructured), improving data readiness for downstream analysis
  • Scaled analytics platform capabilities used by 100+ enterprise clients, enabling faster data-driven decision-making
Software Engineer Intern
Tesla, Inc.
Sep 2022 - Dec 2022 (Concurrent Internship)
Reno, NV
  • Engineered real-time Python ETL pipelines for Powerwall manufacturing data, identifying bottlenecks and reducing adhesive waste by 20%
  • Refined assembly-station logic and data-integrity workflows across 4+ Powerwall configurations, improving operational consistency
  • Established Kubernetes-based CI/CD with Docker and Jenkins across 3 core deployment stages (build, test, and release)
Software Engineering Consultant (Toyota Chile, Ford Argentina)
Independent Consultant
Jan 2018 - Dec 2020
Remote
  • Built an interactive VR product showcase for Toyota Chile, shipping a production marketing experience used in customer-facing campaigns
  • Integrated Ford Argentina APIs into dealership CRM workflows, improving vehicle-order tracking and reliability across multi-dealership sales teams

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