Jobs at Ellison Institute Of Technology, Oxford Limited

Design, build, automate, secure, and operate scalable Oracle Cloud Infrastructure platforms for enterprise, research, and AI workloads. Manage networking, compute, storage, IAM, monitoring, security, backups, disaster recovery, patching, and reliability. Develop Terraform-based infrastructure and Git CI/CD pipelines, support cloud-native teams with reusable patterns, optimize cost and performance, and maintain documentation, standards, and operational procedures.

Build and operate resilient cloud and compute platforms for scientific workloads, including GPU clusters, Kubernetes, networking, storage, CI/CD and GitOps pipelines. Implement Terraform-based infrastructure, Kyverno and YAML Policy-as-Code governance, monitoring, alerting, telemetry and SLOs. Partner with research and data teams to provide self-service capabilities for Python notebooks, ML workloads and distributed computing.

Lead Oracle Fusion Cloud testing and release management across HCM, Finance, and SCM. Own the release calendar, coordinate regression testing and UAT, manage defects and readiness evidence, communicate business impacts, and coordinate production rollouts with internal teams and external suppliers. The role also covers risk-based test planning, release dependencies, supplier handover, AI capability testing, operational acceptance, and continuous improvement of regression coverage and release efficiency.

Leads enterprise financial and operational reporting, business intelligence, data governance, and analytics delivery. Oversees migration from NetSuite and other systems to Oracle Fusion, including data mapping, cleansing, reconciliation, validation, and reporting continuity. Establishes data definitions, quality standards, reporting models, and AI-enabled insight controls. Partners with Finance, operations, technology teams, architects, and implementation partners while leading the reporting team and supporting stakeholder adoption.

Supports the People & Engagement function through portfolio and project management, workforce data analysis, reporting, dashboards, planning, research, and organisational effectiveness initiatives. The role maintains roadmaps, tracks risks and priorities, develops Excel models and presentations, analyses workforce trends, supports HR business partners, and uses AI and digital tools to improve workflows and decision-making.

Build and maintain scalable, reproducible data pipelines for scientific research. Partner with scientists and engineers to ingest, store, curate, transform, version, and deploy biological datasets for machine learning and discovery. Work with cloud infrastructure, containers, distributed processing, GPU compute, and varied scientific data formats while promoting data quality, testing, lineage, schema design, and long-term maintainability.

Build high-throughput data pipelines for autonomous laboratories, integrating robot telemetry, sensor data, video, and instrument signals from edge devices to cloud systems. Develop streaming ingestion, validation, APIs, structured data models, knowledge graphs, provenance, and model-ready datasets. Collaborate with AI researchers, software and robotics engineers, and scientists to support reproducible autonomous experimentation.

Build and maintain robust software systems supporting scientific applications. Responsibilities include designing APIs, CLI tools, software modules, distributed systems, ML and hardware integrations, data pipelines, automated testing, benchmarking, deployment, observability, and CI/CD. Collaborate with data engineers, AI scientists, software engineers, and domain experts while promoting strong engineering, security, and documentation practices across multidisciplinary R&D teams.

Build and operate high-performance GPU clusters for machine learning training and inference. Design high-throughput compute and storage pathways, optimize I/O and data locality, benchmark distributed systems, and resolve performance bottlenecks. Establish observability, resilience, security automation, quotas, and capacity planning while partnering with research and data teams to scale ML experimentation pipelines.

Designs, implements, administers, and supports secure hybrid IT infrastructure across on-premises, Azure, OCI, and other cloud environments. Responsibilities include managing Windows and Linux servers, VMware virtualization, storage, networking, backups, disaster recovery, monitoring, patching, capacity planning, incident resolution, documentation, security controls, and infrastructure automation. The role supports enterprise applications, scientific research, and AI workloads while delivering resilience and service improvements.

Build and operate an end-to-end agentic AI platform for scientists, spanning model endpoints, orchestration, tools, state management, evaluation, observability, failure recovery, APIs, and user interfaces. Work directly with scientists to translate workflows into scalable capabilities, establish technical direction, and shape the product roadmap. The role also requires production-ready, policy-aware systems with durable audit trails and independent ownership from prototype through deployment.

The Microfluidics Engineer/Scientist will design and develop microfluidic systems for diagnostic applications, translating biological assays into automated designs, integrating systems, and conducting experimental validations.