Experience
Experience & capabilities
Years honing the tooling, automation, and operational practices that keep machine learning and data platforms production-ready.
MLOps Role
- Designed and ran ML platforms with MLflow registries, Azure Machine Learning Studio, automated retraining, and drift-aware monitoring.
- Productionised models with blue/green and canary strategies across Kubernetes, serverless, and batch environments.
- Partnered with data scientists to standardise experiment metadata, lineage, and reproducibility.
DevOps delivery
- Built GitHub Actions and Jenkins pipelines for infrastructure, data pipelines, and application services.
- Codified infrastructure with Terraform and Helm across AWS, on-prem, and hybrid estates.
- Led blameless incident response practices backed by robust observability and SLOs.
Oracle DBA expertise
- Handled lifecycle management: installs, upgrades, patching, cloning, and cross-version migrations.
- Implemented RMAN backup strategies, Data Guard, and performance tuning across OLTP and warehouse workloads.
- Introduced security baselines and auditing to meet compliance and uptime targets.
Linux systems operations
- Engineered hardened baselines, configuration management, and golden images for fleet consistency.
- Delivered automation in Bash and Python for provisioning, patching, and fleet drift detection.
- Optimised networking, storage and virtualization stacks to keep latency and throughput predictable.
Scaled ML platform uptime from 92% to 99.8%
Through automated validation gates, progressive delivery and real-time drift dashboards.
Reduced release cycle time by 65%
Introduced trunk-based development, reusable pipeline templates, and compliance automation.
Modernised Oracle estates without downtime
Executed cross-region migrations, performance tuning, and HA rollouts for mission-critical databases.