3 of 3 Permanent Distributed Computing Jobs in the Thames Valley

passion for platform disciplines. Hands-on experience with cloud platforms (AWS, GCP, or Azure), including large-scale ML infrastructure management. Knowledge of GPU computing for model training and serving. Experience managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab … . Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding of monitoring, logging, and observability for large-scale ML systems. Experience in cost optimisation for compute/GPU workloads. Excellent people leadership and communication skills, able to influence technical ...

Experience writing complex joins, stored procedures, and performing performance tuning. Big Data Concepts Foundational understanding of Big Data architecture (Data Lakes, Data Warehouses) and distributed processing concepts (e.g., MapReduce). ETL/ELT Basic knowledge of ETL principles and data modeling (star schema, snowflake schema). Version Control Practical … experience with Git (branching, merging, pull requests). Preferred Qualifications (A Plus) Experience with a distributed computing framework like Apache Spark (using PySpark). Familiarity with cloud data services (AWS S3/Redshift, Azure Data Lake/Synapse, or Google BigQuery/Cloud Storage). Exposure to workflow ...

Spring MVC, and related technologies. Database Technologies: Strong SQL skills; experience with relational databases (e.g., Oracle, SQL Server, PostgreSQL) and/or NoSQL solutions. Distributed Systems: Understanding of distributed computing concepts and technologies (e.g., microservices, caching, clustering). Testing Frameworks: Experience with JUnit, Mockito, or similar testing ...