Jobs at Version 1

Design, build, and operate cloud-native data platforms and reliable ETL/ELT pipelines (batch and streaming) using Databricks and Spark on AWS/Azure. Ensure production readiness—security, testing, monitoring, cost efficiency—and optimise performance, reliability, and data quality. Collaborate with analysts, data scientists and platform teams, mentor junior engineers, support production troubleshooting, and contribute to technical design and operational standards.

Design, build and operate cloud-native data platforms and reliable ETL/ELT pipelines (batch and streaming) primarily on AWS/Databricks. Ensure production readiness (secure, tested, observable, cost-efficient), optimise performance and data quality, collaborate with analysts and data scientists, mentor junior engineers, and support CI/CD, monitoring and operational troubleshooting.

Design, harden, and operate production-grade AI infrastructure and Kubernetes platforms for training, evaluation, and inference. Build MLOps pipelines (versioning, CI/CD, monitoring, rollback), enforce security-first designs, deploy reliable enterprise systems, and accelerate development using AI coding agents while partnering with ML and application teams.

As a Technical Lead in AI, you will architect, design, and deliver enterprise-grade AI systems, focusing on Generative and Agentic AI, while mentoring engineers and collaborating with business stakeholders.

The AI Engineer will design, build, and deploy AI systems, focusing on Generative and Agentic AI, while managing the full lifecycle of AI solutions.

Lead the delivery of data engineering projects by designing and building scalable data platforms; mentor engineers and ensure high-quality outcomes.