David Todd
Verified Expert in Engineering
DevOps Engineer and Developer
Sheffield, United Kingdom
Toptal member since August 14, 2025
David is a highly motivated and results-oriented DevOps/MLOps engineer with over 25 years of experience delivering successful infrastructure, application, and cloud solutions at the enterprise level. He's a certified Google Cloud architect with extensive expertise in Kubernetes, ML/AI infrastructure, and cloud platforms. David has a strong background in designing distributed systems for compute-intensive workloads, focusing on performance optimization and scalability.
Portfolio
Experience
- Python - 10 years
- Terraform - 9 years
- Google Cloud Platform (GCP) - 9 years
- Machine Learning - 6 years
- GitOps - 5 years
- FastAPI - 5 years
- Machine Learning Operations (MLOps) - 3 years
- GitHub Actions - 2 years
Preferred Environment
Linux, Visual Studio Code (VS Code), GitHub, Ubuntu Linux
The most amazing...
...high-performance ML infrastructure I've designed for drug discovery leveraged GKE, NVIDIA GPUs, and CI/CD workflows.
Work Experience
Data Platform Engineer
HSBC UK
- Built and maintained scalable data pipelines using Kubeflow on Google Kubernetes Engine (GKE).
- Developed Python-based automation scripts and APIs for platform management and self-service capabilities.
- Designed infrastructure as code solutions using Terraform for immutable, reproducible environments across development and production.
- Established CI/CD pipelines using Jenkins and Git workflows, enabling automated testing and deployment of data platform components.
- Collaborated with security teams to ensure compliance with financial services regulations, implementing IAM policies and VPC controls.
Senior GCP Platform Engineer
GlaxoSmithKline
- Designed, deployed, and managed cloud-native data services. Enabled self-service environments through secure platform resources and automation, allowing scientists to create governed cloud environments with strict controls for drug discovery.
- Created microservices using Python, FastAPI, and Poetry.
- Built Docker images and scalable deployments with Cloud Run.
- Planned, designed, and built cloud infrastructure.
- Managed cloud security using IAM, roles, and firewall rules.
Senior GCP Platform Engineer
HSBC UK
- Designed scalable ML environments on GCP using IaC for immutable infrastructure. Built data pipelines between on-premises Hive and GCP services (BigQuery, BigTable, Cloud Storage) to train/deploy ML models for financial crime detection.
- Implemented machine learning solutions with Python libraries and Jupyter notebooks.
- Created Docker images and scalable deployments with Google Kubernetes Engine (GKE).
- Planned, designed, and built cloud infrastructure.
- Managed continuous integration/deployment of immutable infrastructure using Jenkins, Terraform, and Ansible.
- Established scalable machine learning solutions with Dataproc, Spark, and Kubeflow.
Senior GCP DataDevOps Engineer
Shell
- Collaborated with data scientists, engineers, and PMs to maintain/deliver GCP environments. Enabled efficient access to structured/unstructured data using IaC for immutable infrastructure. Identified data quality issues and improvement opportunities.
- Built relational database architecture using Google Cloud SQL.
- Created Docker images and scalable deployments with Kubernetes.
- Managed continuous integration/deployment of cloud infrastructure and code using CircleCI.
- Handled platform security using IAM, policies, and roles.
Senior Big Data/Cloud DevOps Engineer
HSBC UK
- Built scalable ML environments in GCP using infrastructure as code for immutable deployments. Handled data processing between on-premise Hive and Google Cloud services, including BigQuery, BigTable, and Cloud Storage.
- Developed machine learning solutions with Python libraries and Jupyter notebooks.
- Created Docker images and scalable deployments with Google Kubernetes Engine (GKE).
- Handled cloud security using IAM, roles, firewall rules, OAuth, and ADFS.
- Planned, designed, and built cloud infrastructure.
- Managed continuous integration/deployment of immutable infrastructure using Jenkins and Ansible.
- Implemented scalable machine learning solutions with Dataproc and Spark.
Senior Data Architect
Telefónica
- Handled Big Data architecture, supporting data mining and predictive analytics. Designed data systems using open/closed source solutions on-premise and in the cloud. Utilized Inmon and Kimball methodologies to analyze vast amounts of data.
- Created, configured, and deployed scalable Hadoop clusters for big data processing and analytics.
- Provisioned Linux and Windows environments in Amazon AWS.
- Implemented fast data movement solutions utilizing Apache Sqoop and PySpark for efficient ETL processes and large-scale data transfers.
- Designed and developed relational database architecture using SQL, Oracle, and PostgreSQL.
Experience
Advent of Code
https://github.com/Jedsman/adventofcodeLLM Router
https://github.com/Jedsman/llm_routerEducation
Master's Degree in Data Science
University of Dundee - Dundee, Scotland
Certifications
Deep Learning Specialization
DeepLearning.AI | via Coursera
Machine Learning Specialization
Stanford University | via Coursera
Professional Machine Learning Engineer Certification
Google Cloud
Professional Cloud Architect Certification
Google Cloud
Professional Data Engineer Certification
Google Cloud
Skills
Libraries/APIs
Python API, Scikit-learn, PyTorch, Spark ML, PySpark, Claude API, OpenAI API
Tools
Terraform, Google Kubernetes Engine (GKE), AI Prompts, GitHub, AutoML, BigQuery, Apache Airflow, Google Cloud Dataproc, Helm, Grafana, Jenkins, Spark SQL, Ansible, NGINX, CircleCI, HashiCorp Vault, Claude, ChatGPT
Languages
SQL, Python, Go, Rust
Platforms
Google Cloud Platform (GCP), Kubernetes, Linux, Visual Studio Code (VS Code), Vertex AI, Kubeflow, Docker, Cloud Run, Ubuntu Linux, Jupyter Notebook, Amazon Web Services (AWS), Amazon EC2
Paradigms
DevOps, OLAP, Continuous Delivery (CD), Continuous Integration (CI), Automation
Frameworks
Hadoop
Storage
Google Cloud Storage, Databases, Data Lakes, Data Pipelines, Apache Hive, Relational Databases, Google Cloud SQL, Cloud Firestore, Amazon S3 (AWS S3), HBase, MongoDB, Amazon DynamoDB
Other
Back-end, API Integration, FastAPI, GitOps, GitHub Actions, Machine Learning, Machine Learning Operations (MLOps), APIs, Statistics, Neural Networks, Natural Language Processing (NLP), Tf-idf, Data Processing, Scalability, Machine Learning (ML) APIs, Responsible AI, Cloud Architecture, Cloud Computing, Cloud Security, Identity & Access Management (IAM), Networking, Workload Migration, Google BigQuery, Data Engineering, Data Processing Systems (DPS), Data Security, Data Transformation, Data Warehousing, Data Build Tool (dbt), Pub/Sub, Prometheus, DataOps, Containers, Data Architecture, Continuous Testing (CT), Large Language Model Operations (LLMOps), Argo CD, Consul, Groovy Scripting, Amazon Redshift, Data Science, Anthropic, Gemini, Gemini API, OpenAI, Deep Learning, Hyperparameters, Model Regularization, Convolutional Neural Networks (CNNs), Sequence Models, Supervised Machine Learning, Linear Regression, Classification, Algorithms, Unsupervised Machine Learning, Recommendation Systems, Reinforcement Learning, Artificial Intelligence (AI)
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