Digital Eng, Robotics & Automation, Process Sciences, Analytics and Technology, CGT

We are seeking a Engineer, Digital and Data Science – Robotics and Automation within Process Sciences, Analytics and Technology, Cell and Gene Therapy. This engineer role will execute software, data, systems-integration, and digital-connectivity work required to develop and operate advanced robotic and automation platforms supporting AZD0120 and future cell and gene therapy programs. This engineer will build and support reliable interfaces, reusable software components, data pipelines, workflow logic, monitoring tools, and technical documentation that connect robotics with laboratory, manufacturing, and enterprise systems. The role will support platform scaling and the transition of robotic and digitally enabled workflows from laboratory development through manufacturing implementation and deployment for future commercial production. The engineer will contribute to reusable digital, data, and automation architectures that improve standardization, reliability, maintainability, technology transfer, lifecycle support, and expansion across cell therapy programs. The successful candidate will work hands-on in laboratory and engineering environments with automation engineers, scientists, Manufacturing Science and Technology, QC, Manufacturing Operations, Quality, Digital, IT, Informatics, and external technology partners. The engineer will directly support laboratory testing, engineering runs, workflow execution, equipment operation, troubleshooting, and data review in addition to software and data responsibilities. A central expectation is disciplined collaboration with Digital and IT so that locally useful solutions are also secure, supportable, governed, interoperable, and aligned with enterprise architecture and data standards. This position reports to the Executive Director, Automation, Robotics & Technical Writing and is based in Tarzana, California. Accountabilities Software Engineering for Robotics and Automation · Develop, configure, test, deploy, and maintain software components, scripts, utilities, services, and interfaces supporting robotic and modular automation workflows. · Implement equipment and instrument connectivity using approved APIs, software development kits, middleware, message-based integration, database interfaces, equipment drivers, serial communication, or industrial protocols, as applicable. · Contribute to workflow orchestration, scheduling logic, device-state management, handshakes, alarms, exception handling, pause-and-resume behavior, and recovery pathways. · Apply maintainable software-engineering practices using Digital- and IT-approved technology stacks, development environments, repositories, source-control and peer-review practices, automated testing, CI/CD approaches, release documentation, configuration management, and controlled deployment. · Develop within approved enterprise technology stacks and integration patterns, which may include Git-based repositories, Azure DevOps or comparable lifecycle tooling, APIs, containerized applications, cloud services, automated test frameworks, observability tooling, and software release-management capabilities, as appropriate to the intended use and support model. · Troubleshoot software defects, interface failures, timing issues, data mismatches, device-state conflicts, and workflow interruptions using logs, diagnostics, test evidence, and structured root-cause analysis. · Create reusable integration patterns and libraries that reduce one-off customization and improve scalability across automation platforms. Data Engineering, Contextualization and Traceability · Design and maintain data pipelines that capture, validate, transform, contextualize, store, and expose robotics- and equipment-generated data for authorized scientific, engineering, and operational use. · Define and implement data structures for process parameters, equipment states, alarms, workflow events, sample and material identity, metadata, user actions, timestamps, and execution history. · Support end-to-end sample and material lineage, data provenance, auditability, and traceability across connected workflows and systems. · Build dashboards, automated reports, alerts, and engineering views that provide visibility into workflow status, failures, cycle time, utilization, interventions, and system performance without replacing accountable business or quality records. · Perform data-quality checks and support reconciliation of missing, duplicated, delayed, incorrectly mapped, or out-of-range data. · Partner with data platform owners to align interfaces, metadata, retention, access, and data-product practices with enterprise standards. Digital and IT Partnership, Architecture and Cybersecurity · Work with Digital, IT, Informatics, system owners, and enterprise architects to translate scientific and automation needs into secure, supportable software and data solutions. · Bridge Operational Technology (OT), Laboratory Technology (LT), and Information Technology (IT) environments to enable secure, interoperable, and scalable automation ecosystems spanning laboratory, manufacturing, and enterprise systems. · Contribute to interface contracts, data-flow diagrams, network and hosting requirements, identity and access models, environment strategies, backup and recovery expectations, monitoring, and support handoffs. · Collaborate with Manufacturing, Quality, Digital, and IT to integrate robotics and automation platforms with manufacturing execution systems (MES), electronic batch records, LIMS, process historians, manufacturing analytics platforms, and related systems supporting cGMP operations. · Use approved development environments, repositories, cloud services, integration patterns, and deployment processes; avoid unsupported shadow systems and unmanaged production dependencies. · Support cybersecurity reviews, threat and vulnerability remediation, least-privilege access, secrets management, patching coordination, logging, incident support, and lifecycle planning for connected automation assets. · Clearly define ownership boundaries among PSAT, vendors, Digital, IT, Quality, and business system owners, including escalation pathways and operational support responsibilities. AI-Enabled Robotics and Advanced Analytics · Collaborate with Digital, IT, Data Science, Quality, and scientific SMEs to evaluate AI/ML use cases for robotics monitoring, anomaly detection, predictive maintenance, computer vision, scheduling, workflow optimization, and operator decision support. · Support development and application of digital twins, simulation environments, virtual commissioning models, workflow emulation, and robotics or process models that improve design evaluation, capacity planning, troubleshooting, optimization, deployment readiness, and lifecycle improvement. · Evaluate and support computer-vision capabilities for robotic guidance, barcode and label verification, object and container identification, material tracking, automated inspection, anomaly detection, and workflow monitoring, subject to defined intended use, testing, and human oversight. · Prepare trusted, contextualized, and appropriately governed data suitable for model development, evaluation, and monitoring. · Prototype AI-enabled capabilities only within approved environments and with defined intended use, acceptance criteria, human oversight, source traceability, access controls, and lifecycle ownership. · Evaluate model and workflow performance using documented test data; identify limitations, false positives, drift, unsupported outputs, and operational risks before broader deployment. · Ensure AI outputs r