• Contribute to building and designing the self-service platform • Architect and build self-service infrastructure frameworks • Codify and abstract AWS footprint into reusable infrastructure-as-code modules • Build and implement evaluation frameworks and observability practices • Evaluate ecosystem needs holistically to spot platform investments • Align architectural deliverables with application workflows • Provide technical escalation availability during agreed windows

Role Description We are looking for AWS Cloud Data Platform Engineers to join a project delivered for our international client operating in a data-driven and cloud-first environment. The client is building modern cloud-native data platforms that enable advanced analytics, business intelligence, and data-driven decision-making across the organization. This role is a great opportunity to work on scalable AWS-based data platforms, contributing to the design, development, and continuous improvement of modern data ecosystems. You will combine cloud platform engineering, data engineering, and software development to deliver reliable, secure, and high-performance data solutions supporting business-critical processes. The project combines cloud technologies, data platform development, ETL/ELT engineering, and platform automation within a collaborative international environment. You will work closely with business stakeholders, architects, data engineers, and software development teams to deliver scalable and maintainable cloud-based data solutions. - Design, develop, and maintain modern data platforms based on AWS cloud services. - Build and optimize data lakes, lakehouses, and data warehouse solutions. - Develop and maintain ETL and ELT pipelines for batch and streaming data processing. - Integrate data from multiple sources, including databases, APIs, and event-driven systems. - Ensure data quality, reliability, observability, and operational stability across data platforms. - Develop data processing solutions using Python, SQL, Spark, and related technologies. - Work with AWS services such as S3, Glue, Lambda, EMR, Redshift, Kinesis, and similar cloud-native services. - Implement and maintain workflow orchestration using technologies such as Apache Airflow, AWS Step Functions, or equivalent solutions. - Contribute to CI/CD pipelines and deployment automation for data engineering workloads. - Monitor platform health, troubleshoot data-related incidents, and implement preventive improvements. - Optimize platform performance, scalability, and cloud cost efficiency. - Collaborate closely with business stakeholders, architects, analysts, and engineering teams to deliver high-quality data products. - Contribute to engineering best practices, platform standards, automation, and continuous improvement initiatives. - Depending on seniority, take ownership of technical areas, support architectural decisions, and mentor less experienced team members. Qualifications - Commercial experience in data engineering, cloud engineering, or data platform development. - Strong hands-on experience with AWS cloud services. - Practical experience designing and building cloud-based data platforms. - Experience developing data lakes, lakehouses, or enterprise data warehouse solutions. - Strong experience building ETL and ELT pipelines in production environments. - Practical experience integrating multiple data sources, including relational databases, APIs, and streaming platforms. - Strong programming skills in Python and SQL. - Experience with Apache Spark or similar distributed data processing frameworks. - Practical experience with AWS services such as S3, Glue, Lambda, EMR, Redshift, Kinesis, or equivalent technologies. - Experience implementing workflow orchestration using Apache Airflow, AWS Step Functions, or similar tools. - Understanding of CI/CD practices and deployment automation for cloud-based data solutions. - Experience implementing monitoring, observability, and operational support for data platforms. - Strong understanding of cloud performance optimization and cost management. - Strong communication, collaboration, and problem-solving skills. - Experience working within Agile software delivery environments. - Practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery. - Work from the European Union region and a work permit are required. Nice to have - Experience working with real-time or event-driven data processing architectures. - Experience with Infrastructure as Code technologies such as Terraform or AWS CloudFormation. - Familiarity with containerization and orchestration technologies such as Docker and Kubernetes. - Experience implementing data governance, security, and compliance practices. - Experience contributing to enterprise-scale cloud data platform architecture. - Experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work. - Interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches. Recruitment Process - CV review - HR call - Interview - Client Interview - Decision

Role Description We are seeking an experienced OpenShift Engineer to design, deploy, and operate enterprise-grade Red Hat OpenShift container platforms running mission-critical workloads across on-premises and cloud environments. In this role you will own the end-to-end platform lifecycle — from cluster provisioning and hardening, through workload onboarding and developer enablement, to ongoing operations and continuous improvement. The ideal candidate will bring deep expertise in OpenShift and Kubernetes internals, strong Linux fundamentals, and a security-first mindset, and will partner with application teams, security engineers, and infrastructure stakeholders to deliver a stable, secure, and developer-friendly container platform. Key Responsibilities - Architect, provision, and operate Red Hat OpenShift clusters across on-premises, virtualized, and major cloud environments, applying best practices for high availability, scalability, and security. - Design and implement multi-tenant OpenShift platforms with appropriate project isolation, network policies, quotas, RBAC, and security context constraints. - Automate cluster lifecycle operations — installation, upgrades, scaling, and decommissioning — using OpenShift installer, GitOps tooling (Argo CD, OpenShift GitOps), and IaC frameworks. - Onboard application development teams to the platform, providing clear self-service patterns, project templates, CI/CD integrations, and supporting documentation. - Operate OpenShift networking, including SDN/OVN-Kubernetes, ingress controllers, routes, network policies, and integration with enterprise firewalls and load balancers. - Manage OpenShift storage strategies (block, file, object), including dynamic provisioning, OpenShift Data Foundation, and integration with external storage platforms. - Implement comprehensive monitoring, logging, and tracing using the OpenShift observability stack (Prometheus, Grafana, EFK, Tempo) and integrate with enterprise SIEM and APM tools. - Harden the platform in collaboration with security teams, including image scanning (Quay/Clair), runtime protection, secret management, and continuous compliance with regulatory frameworks. - Design and maintain robust CI/CD pipelines on OpenShift using Tekton, Jenkins, GitLab CI, or Argo Workflows, enabling safe and frequent application deployments. - Develop disaster-recovery and backup strategies for cluster state, etcd, and persistent workloads, validated through regular failover and restore drills. - Plan, test, and execute cluster upgrades across minor and major versions with minimal disruption to workloads, including pre-upgrade validation and rollback strategies. - Partner with application teams to right-size workloads, manage resource quotas, and tune cluster autoscaling for predictable performance and cost efficiency. - Continuously evolve platform tooling and developer experience, capturing feedback from internal users and translating it into roadmap improvements. - Mentor junior engineers and act as an OpenShift subject-matter expert across the broader organization. Qualifications - Bachelor’s degree in Computer Science, Engineering, or a related technical discipline. - Five or more years of experience operating container platforms in production, with at least three years on Red Hat OpenShift. - Deep, hands-on knowledge of Kubernetes and OpenShift internals, including operators, CRDs, RBAC, and networking. - Strong Linux administration skills, including networking, performance tuning, and troubleshooting. - Hands-on experience with infrastructure-as-code tools such as Ansible, Terraform, or Helm. - Solid experience implementing CI/CD pipelines on OpenShift or Kubernetes using Tekton, Jenkins, or Argo CD. - Strong scripting skills in Bash, Python, or Go. - Working knowledge of cluster monitoring, logging, and tracing tools. - Familiarity with container image security and supply chain hardening. - Excellent troubleshooting, communication, and documentation skills. Preferred Qualifications - Red Hat Certified Specialist in OpenShift Administration or Architect-level Red Hat certification. - Experience operating OpenShift on at least one major public cloud (AWS, Azure, GCP). - Exposure to service mesh implementations (OpenShift Service Mesh, Istio, Linkerd). - Familiarity with regulated environments such as PCI-DSS, HIPAA, or SOC 2. - Experience with GitOps workflows using Argo CD or Flux. Requirements - 5+ years of experience. - No new H1B sponsorship available. H1B transfers welcomed for qualified candidates. - Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party). - Long-term, multi-year engagement aligned to the Bright Vision SOW delivery roadmap. Benefits - Competitive base salary commensurate with experience, plus benefits. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 650-6699.

Role Description For decades, Cadmould has been one of the fastest and most advanced injection molding simulators on the market, trusted across the plastics industry. Then we built something the field had never seen. Cadmould AI Solver is the first Large Engineering Model (LEM) for plastic injection molding: a transformer-based neural physics model that delivers high-fidelity results up to 1,000x faster than classical solvers. It turns simulation from a slow validation step into something engineers can explore in real time. It's live as a research preview on our site, and it has already shipped to our first customers. Powerful models are only as good as the data they learn from, and only matter once they ship. That's your domain. You'll treat training data as a first-class asset: versioned, traceable, and continuously improved, with its impact on results made visible. You'll build the pipelines, the model lifecycle, and the live AWS service that carry our models from experiment to customers. The systems around the models are as much the product as the models themselves. This role can be performed from our office in Würselen near Aachen or remotely from Germany, with occasional travel for team events and on-sites. What You Will Do - Build and own the platform behind our AI Solver: the systems that manage our training data and models as first-class assets, bring them reliably into production, and serve them to customers. - Build the training and data platform. - Design the pipelines and systems that version, track, and manage our training data and models as the assets they are, with reproducibility and lineage built in. - Own the model lifecycle. - Build the path from experiment to production: model versioning, a registry, promotion, and reliable, repeatable training and deployment. - Close the loop in production. - Build the monitoring that surfaces model degradation and flags when incoming data drifts outside what a model handles well, so our AI engineers know where to act. - Enable the AI team. - Provide the workflows and tooling our AI engineers and data scientists use to train, evaluate, and deploy models. - Run and evolve the production service. - Operate and scale our AWS service that serves the AI models, keep it fast and reliable, and extend it as we grow, for example from serving a single model to multiple selectable models, including access-controlled or user-specific ones. - Work hand in hand with the Cloud team. - Pitch in where it counts. Qualifications - Background in Computer Science, Data Engineering, Machine Learning, or a related field, with 3+ years of relevant experience. - Strong Python skills and solid software engineering fundamentals (testing, version control, CI/CD). - Hands-on experience taking ML systems from training into production: data pipelines, training workflows, and deployment. - Experience with cloud environments and containerization (AWS, Docker, Kubernetes, or similar). - Familiarity with experiment tracking and model/data versioning tools (e.g., MLflow, Weights & Biases, DVC). - Pragmatic and reliability-minded. - English is our working language; German is a plus. Requirements - Exposure to scientific computing or simulation data. - Deploying AI models beyond the cloud: CPU-only on-premises or edge targets, and hybrid setups. - Workflow orchestration (Airflow, Prefect, or similar). - Inference optimization (quantization, pruning, efficient architectures). - AWS stack (S3, EC2, ECR, SageMaker) and infrastructure as code (Terraform). - Building internal platforms or tooling that other engineers build on. Benefits - A real technical challenge. - Ownership and impact. - Modern tooling. - Direct and honest culture.