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I'm John Verran, a Senior AI/ML Architect and Data Engineer with 12+ years in designing, building, and deploying production-grade ML and data systems. I specialize in end-to-end ML platforms, ETL pipelines, big data processing, MLOps, and model deployment at scale across healthcare, finance, and SaaS. I thrive at the intersection of data engineering and ML engineering, delivering robust, scalable platforms. I partner with stakeholders to translate business needs into practical AI-powered solutions, using AWS, Azure, and GCP, along with tools like Kafka, Spark, MLflow, and LangChain to drive impact.…I'm John Verran, a Senior AI/ML Architect and Data Engineer with 12+ years in designing, building, and deploying production-grade ML and data systems. I specialize in end-to-end ML platforms, ETL pipelines, big data processing, MLOps, and model deployment at scale across healthcare, finance, and SaaS. I thrive at the intersection of data engineering and ML engineering, delivering robust, scalable platforms. I partner with stakeholders to translate business needs into practical AI-powered solutions, using AWS, Azure, and GCP, along with tools like Kafka, Spark, MLflow, and LangChain to drive impact.

John Verran

Data Scientist, AI Engineer, Data Analyst, +2

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I'm John Verran, a Senior AI/ML Architect and Data Engineer with 12+ years in designing, building, and deploying production-grade ML and data systems. I specialize in end-to-end ML platforms, ETL pipelines, big data processing, MLOps, and model deployment at scale across healthcare, finance, and SaaS. I thrive at the intersection of data engineering and ML engineering, delivering robust, scalable platforms. I partner with stakeholders to translate business needs into practical AI-powered solutions, using AWS, Azure, and GCP, along with tools like Kafka, Spark, MLflow, and LangChain to drive impact.…I'm John Verran, a Senior AI/ML Architect and Data Engineer with 12+ years in designing, building, and deploying production-grade ML and data systems. I specialize in end-to-end ML platforms, ETL pipelines, big data processing, MLOps, and model deployment at scale across healthcare, finance, and SaaS. I thrive at the intersection of data engineering and ML engineering, delivering robust, scalable platforms. I partner with stakeholders to translate business needs into practical AI-powered solutions, using AWS, Azure, and GCP, along with tools like Kafka, Spark, MLflow, and LangChain to drive impact.

Available to hire

I’m John Verran, a Senior AI/ML Architect and Data Engineer with 12+ years in designing, building, and deploying production-grade ML and data systems. I specialize in end-to-end ML platforms, ETL pipelines, big data processing, MLOps, and model deployment at scale across healthcare, finance, and SaaS.

I thrive at the intersection of data engineering and ML engineering, delivering robust, scalable platforms. I partner with stakeholders to translate business needs into practical AI-powered solutions, using AWS, Azure, and GCP, along with tools like Kafka, Spark, MLflow, and LangChain to drive impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Principal Data Engineer at Epiminder

October 1, 2022 - Present

Architected and implemented a scalable ML platform combining Apache Kafka, Spark, and AWS SageMaker to support predictive modeling for patient outcomes across millions of health records. Designed MLOps pipelines with Docker, Kubernetes, Terraform, and MLflow, ensuring reproducibility, automated retraining, and 99.9% uptime in production environments. Led a cross-functional team of 7 engineers and data scientists, delivering LLM-powered automation systems with LangChain and OpenAI APIs to optimize patient engagement and care coordination. Built real-time prediction APIs using FastAPI and gRPC deployed on Kubernetes with autoscaling. Partnered with business stakeholders to deliver executive dashboards linking model outputs into decision workflows. Optimized cloud costs by 18% through workload optimization and endpoint right-sizing.

Senior Machine Learning Engineer at Wesfarmers OneDigital

July 1, 2020 - October 1, 2022

Designed and deployed NLP models using Transformer architectures for clinical risk scoring and early disease detection. Built ETL pipelines on AWS (S3, Lambda, EC2, Glue) to process terabytes of structured (EHR) and unstructured (clinical notes, imaging metadata) health data. Established cloud-native training environments with automated hyperparameter tuning, distributed training, and reproducibility tracking using MLflow. Automated model retraining pipelines triggered by new data ingestion, reducing model drift and improving accuracy over time. Collaborated with data governance and HIPAA compliance teams to ensure ML workflows met regulatory requirements. Delivered AI-powered diagnostic tools with real-time clinical decision support dashboards used by physicians and analysts. Reduced ETL processing time by 40% by refactoring Spark jobs and optimizing partition strategies.

Senior Machine Learning Engineer at Veolia UK

March 1, 2018 - July 1, 2020

Developed and optimized data pipelines for time-series financial data, improving support for risk scoring and fraud detection models. Guided business analysts in SQL and data visualization best practices, improving data accessibility and adoption across departments. Implemented slowly changing dimensions (SCD Type 2 and Type 4) to improve historical accuracy of customer and transaction data. Automated ETL scheduling and monitoring with Apache Airflow, reducing manual interventions. Partnered with IT teams to align data warehouse strategy with enterprise reporting needs.

Senior Machine Learning Engineer at Deloitte Australia

March 1, 2014 - March 1, 2017

Conducted statistical validation and retraining of segmentation models for customer targeting. Built ETL processes to integrate multiple internal systems into the enterprise data warehouse. Enhanced time-series analysis workflows supporting credit risk prediction. Produced documentation and reusable data engineering templates to improve onboarding and standardization across teams. Designed and maintained data pipelines to support ML-driven analytics across financial services products. Conducted statistical testing to validate segmentation models for product recommendations. Provided technical mentorship to junior analysts in SQL, statistics, and visualization. Built and maintained a library of ETL templates, dashboards, and documentation, reducing onboarding time by 30%. Coordinated with data scientists to deliver clean, production-ready datasets for machine learning experiments.