Jobs at Aligned Automation

Design, build, and maintain scalable MLOps platforms and CI/CD pipelines. Containerize and orchestrate workloads, implement model versioning and monitoring, deploy models as APIs, optimize serving performance, and enforce security and governance while collaborating with data and platform teams to productionize ML solutions.

Design, build, and maintain scalable Azure cloud data platforms using Databricks and Microsoft Fabric. Develop ETL/ELT pipelines with PySpark, Spark SQL, and Delta Lake; implement Lakehouse and data warehouse architectures; perform ingestion, transformation, validation, and performance tuning; collaborate with stakeholders; support deployments, code reviews, CI/CD, governance, and production support.

Certified Scrum Master to lead cross-functional Agile teams, facilitate Scrum ceremonies, coach Agile practices, remove impediments, manage stakeholders and backlogs, track Agile metrics, maintain project/risk plans, and drive on-time high-quality delivery while promoting continuous improvement and an Agile mindset.

Lead DevOps engineering for an MES programme: design and run CI/CD pipelines, manage toolchain and environments (Syncade), implement IaC and monitoring, support CSV/GxP validation, improve deployment metrics, and act as escalation authority for infrastructure and disaster recovery.

Own product vision, roadmap, and delivery of Microsoft Power Platform solutions. Lead discovery, translate needs into user stories, manage backlog, enforce governance, drive adoption, and collaborate with stakeholders and delivery teams to deliver scalable low-code automation and analytics.

Serve as SME for enterprise hardware break/fix support, manage ticket queues and dispatches, handle escalations, mentor junior engineers, produce documentation and KB articles, analyze recurring issues, collaborate with field/engineering/product teams, and report SLA and operational metrics while supporting 24×7 regional coverage.

Lead procurement digital transformation programs using analytics, automation, and strategic sourcing to drive cost savings, implement procurement platforms (e.g., SAP Ariba), develop dashboards, identify AI/RPA opportunities, manage stakeholders globally, and track program benefits including sustainability initiatives.

Lead programme governance, delivery excellence, and AI-augmented PMO operations across enterprise clients. Coordinate between technical teams and executives, manage planning, reporting, documentation, incident/problem analysis, risk and change management, and continuous improvement while maintaining high quality and stakeholder communication.

Lead QA for MES implementations: define testing strategy, lead test team, create and execute test plans (functional, regression, integration, performance, UAT), integrate automated tests into CI/CD, act as quality gate for releases, support CSV/GxP activities, manage defects, and report testing metrics to stakeholders.

Lead implementation and support of site infrastructure for business and manufacturing workstations, peripherals, printers and telecom. Perform technical requirements gathering, cybersecurity assessments, troubleshooting (Citrix, VDI, RealVNC, Linux, macOS, Windows, iGel), DNS/NTP/Proxy configuration, validation/testing support to assure GMP data integrity, user training, and drive service management and continuous improvement per ITIL.

Lead end-to-end cross-functional projects and programs, synthesize data into insights, and produce executive-quality PowerPoint presentations. Validate datasets, manage stakeholders, track action items, and maintain documentation and dashboards. Use AI prompt-engineering tools to accelerate research and improve deliverables while driving alignment and securing leadership sign-off.

Entry-level role for graduates to build and support data pipelines, ETL, data cleansing, transformation, validation, and modeling. Develop reports, dashboards and visualizations, collaborate with teams to meet business requirements, participate in AI/ML initiatives, document processes, and contribute to automation and continuous improvement using cloud and modern data tools.

Design, build, and deploy production-grade AWS data services and ETL pipelines using PySpark and AWS Glue. Perform data modeling, data warehousing, and advanced SQL development; automate CI/CD and Lambda workflows; work with Redshift/RDS and large datasets to extract and transform data for analytics.

Execute strategic sourcing for technical services and materials across EU sites. Perform TCO analysis, market and risk assessments, negotiate multi-site agreements, implement sourcing plans, measure supplier performance, support procurement transformation, and ensure ethical and financial compliance while collaborating with category managers and stakeholders.

Lead enterprise data strategy and architecture for scalable, secure, AI-ready lakehouse/data platforms. Define governance, quality, observability, metadata/lineage, and data security. Enable AI/ML and GenAI use cases, advise senior stakeholders, and mentor teams.

Lead the MES recipe development workstream: design and configure recipes, enforce standards, manage version control and migrations, support testing (FAT), ensure GxP/validation compliance, and mentor the development team.

Lead enterprise AI-native transformation by designing and deploying multi-agent systems, agent orchestration, and RAG-based workflows. Work with executives to translate strategy into production-grade prototypes, scalable accelerators, and human-in-the-loop governance to improve portfolio management, delivery orchestration, and operational intelligence.

Lead end-to-end AI/ML-driven transformation projects: translate business requirements, design experiments, analyze results, operationalize models with data science and engineering teams, drive agile delivery, stakeholder alignment, and mentor teams to deliver measurable business impact.

Configure and develop MES recipes, workflows, and batch execution logic; implement electronic batch records (EBR); translate functional requirements into configuration; perform unit, integration, and FAT testing; maintain version control and configuration migrations; resolve defects and ensure GxP validation compliance.

Lead end-to-end AI/ML-driven transformation projects: gather and translate requirements, design experiments, analyze results, partner with data science and engineering to operationalize models, drive agile project delivery, stakeholder alignment, and mentor teams to ensure measurable business impact.