• Act as the lead engineer on Product Engineering epics and stories. • Develop (“operationalize”) new repeatable Ensono public cloud services and solutions. • Define and evolve public cloud technical standards to ensure repeatability, supportability, scalability, and security. • Make recommendations on technical stack including research, assessments, bake-offs, and proofs of concepts. • Assess and onboard new AWS and GCP services in line with the Product roadmap. • Drive the deployment of automated infrastructure and systems using IAC, config management and scripting. • Provide subject matter expertise and advice to pre-sales architects on our technical standards. • Act as a final line of escalation and assistance for the cloud operations team for major problems. • Proactively keep up to date on public cloud services and related technology.

• Act as the lead engineer on Product Engineering epics and stories. • Develop (“operationalize”) new repeatable Ensono public cloud services and solutions. • Define and evolve public cloud technical standards to ensure repeatability, supportability, scalability, and security. • Make recommendations on technical stack including research, assessments, bake-offs, and proof of concepts. • Assess and onboard new AWS and GCP services in line with the Product roadmap. • Drive the deployment of automated infrastructure and systems using IAC, config management, and scripting. • Provide subject matter expertise and advice to pre-sales architects on our technical standards. • Act as a final line of escalation and assistance for the cloud operations team for major problems. • Proactively keep up to date on public cloud services and related technology.

Role Description We are looking for an experienced Senior Cloud Architect with a strong engineering background and deep experience in multi-cloud infrastructure (AWS, Azure, GCP), including Kubernetes-based platforms. You will partner closely with customers, SRE teams, and internal stakeholders to design reliable cloud architectures that accelerate delivery pipelines and simplify operations. - Champion automation, build reusable IaC templates and utility tools, design secure cloud networks, and drive customer success. - Combine hands-on engineering depth with architectural leadership to operate platforms at scale. - Help the team adopt emerging AI capabilities as a growth area. Qualifications - Extensive experience designing and operating enterprise-scale cloud platforms. - Deep AWS expertise with working knowledge of Azure and/or GCP. - Strong understanding of multi-region, highly available, and disaster recovery architectures. - Experience with cloud migrations, modernization programs, and AWS Well-Architected Framework reviews. - Knowledge of AWS Organizations, Control Tower, multi-account strategies, and landing zones. - Strong knowledge of cloud networking, TCP/IP, routing, DNS, VPNs, and hybrid connectivity. - Expertise with AWS networking services including VPC, Transit Gateway, Direct Connect, Route 53, CloudFront, PrivateLink, and Load Balancers. - Experience designing hybrid and multi-cloud network architectures. - Understanding of network security, zero-trust principles, and microsegmentation. - Hands-on Kubernetes experience in production environments. - Expertise with EKS and at least one additional platform (AKS, GKE, or OpenShift). - Experience with cluster lifecycle management, multitenancy, service mesh, networking, and workload identity. - Strong GitOps experience (ArgoCD/Flux), Helm, and Kustomize. - Advanced Terraform skills and experience building reusable IaC modules. - Strong CI/CD, GitOps, and automation experience. - Ability to create operational tooling, self-service platforms, and policy-as-code solutions. - Strong software engineering background with proficiency in Python or similar languages. - Experience with DevOps and SRE practices including observability, SLIs/SLOs, incident management, and toil reduction. - Ability to participate in critical incident response and drive operational improvements. - Strong understanding of AWS security services, IAM, encryption, and compliance frameworks (GDPR, ISO 27001, CIS). - Experience with FinOps, cloud cost optimization, TCO analysis, and budgeting. - Interest in applying AI and Generative AI to cloud operations and automation. - Familiarity with services such as Amazon Bedrock, SageMaker, or similar platforms is a plus. - Strong customer-facing communication and stakeholder management skills. - Ability to translate business requirements into scalable technical solutions. - Fluent English required; German is a plus. Requirements - You are an experienced Cloud Architect with a strong engineering mindset and deep expertise in AWS, networking, Kubernetes, and automation. - You enjoy solving complex platform challenges, working directly with customers, building reusable solutions, and driving operational excellence. - You combine technical depth with strong communication skills and are passionate about cloud-native technologies, automation, and continuous improvement. Benefits - Remote working possibility is only available within Hungary due to European taxation regulation. Company Description As Hungary’s most attractive employer in 2025 (according to Randstad’s representative survey), Deutsche Telekom IT Solutions is a subsidiary of the Deutsche Telekom Group. The company provides a wide portfolio of IT and telecommunications services with more than 5300 employees. We have hundreds of large customers, corporations in Germany and in other European countries. DT-ITS received the Best in Educational Cooperation award from HIPA in 2019, acknowledged as the Most Ethical Multinational Company in 2019. The company continuously develops its four sites in Budapest, Debrecen, Pécs, and Szeged and is looking for skilled IT professionals to join its team.

Title:Cloud Engineer Location: Los Angeles United States Work Location: Los Angeles, CA, USA Flexible Hybrid Job Description: Primary Duties and Responsibilities Press space or enter keys to toggle section visibility The Cloud Engineer will design, build, and operate infrastructure and applications supporting UCLA Health’s Analytics Platform across both on-premises and multi-cloud environments (AWS, Azure, GCP). This role focuses on enabling secure, scalable AI/ML and GenAI platforms, with an emphasis on automation, reliability, and compliance in a regulated healthcare setting. Key Responsibilities - Design, implement, and manage cloud and hybrid infrastructure supporting analytics and AI/ML workloads - Build and operate MLOps capabilities, including: - Model training and inference platforms - Model and artifact management - CI/CD and deployment pipelines - Observability and monitoring solutions - Cost optimization controls - Develop and maintain automation and infrastructure-as-code (IaC) solutions for provisioning and configuration - Troubleshoot and resolve complex system and environment issues across cloud and on-prem platforms - Establish platform guardrails to ensure secure, reliable, and compliant operations - Collaborate with cross-functional teams to: - Gather requirements - Design and prototype solutions - Implement and test deployments - Support ongoing operations and enhancements - Apply security, privacy, and governance controls aligned with healthcare data regulations - Execute release, deployment, and configuration management processes What You’ll Bring - Strong background in cloud engineering and platform operations - Experience with multi-cloud environments (AWS, Azure, GCP) - Proficiency in: - Automation, scripting, and infrastructure-as-code - CI/CD pipeline development and optimization - Monitoring and observability tools - Experience supporting AI/ML or data platform workloads (preferred) - Ability to troubleshoot complex systems and drive solutions independently - Strong collaboration skills and the ability to translate business requirements into technical solutions Salary Range: $128500 - $298100 annually. Job Qualifications Press space or enter keys to toggle section visibility •  BS/MS in Computer Science (or equivalent) •  AWS Certified Cloud Engineer, Architect, Administrator Certifications required •  7+ years of advanced knowledge and experience as an AWS Cloud Engineer in all core services and offerings. AWS experience a plus •  15+ years of advanced knowledge and experience of Microsoft Technologies such as, Windows server and Linux based servers, enterprise system support experience and strong background in systems engineering and administration for both operating systems •  15+ years of advanced knowledge and experience with enterprise scale Windows technologies such as Server platforms, Desktop platforms, Exchange Environments, Active Directory, IIS, Windows Clustering, Virtualization and Collaboration tools. AWS Certification or equivalent experience preferred •  Working knowledge of DevOps-like work or experience in a real time operational role •  Advanced knowledge of analytics and AI/ML platform services across AWS, Azure, and GCP (e.g., AWS SageMaker/Bedrock, Azure Machine Learning/Azure OpenAI, Google Vertex AI) and how to operate them securely at enterprise scale. •  Experience enabling teams to build and deploy ML/AI solutions by providing reusable platform capabilities (reference architectures, templates, SDK/CLI standards, self-service onboarding, and guardrails) rather than only project-specific implementations. •  Hands-on experience operationalizing ML/AI workloads on cloud platforms (AWS/Azure/GCP): managed training/inference, batch vs real-time serving, feature/metadata management, model registry, and cost/performance optimization. •  Strong MLOps/platform engineering experience: CI/CD for ML and GenAI, automated validation gates, reproducible pipelines, environment promotion, artifact/version management, and production monitoring (drift, data quality, latency, cost) using cloud-native and/or enterprise tooling (e.g., Azure DevOps/GitHub Actions, SageMaker Pipelines, Vertex AI Pipelines, MLflow, Terraform). •  GenAI platform experience (AWS/Azure/GCP): deploying and governing LLM applications using managed services (e.g., Bedrock/Azure OpenAI/Vertex AI), RAG architectures, embeddings and vector databases/search, prompt/version management, and evaluation/guardrails for safety and groundedness. •  Responsible AI + governance experience for regulated environments: PHI/PII protections, access controls, encryption and key management, audit logging, model/endpoint risk assessments, bias/fairness considerations, and policy enforcement aligned to HIPAA and secure SDLC. •  Strong data engineering foundations that support AI platforms: standardized data ingestion/ETL/ELT, data quality/lineage, dataset and feature pipeline design, schema/version management, and integration with lake/lakehouse platforms (e.g., S3/ADLS/GCS with Spark/Databricks/BigQuery/Synapse) for feature and training data readiness. •  Experience operating scalable training/inference platforms (GPU/accelerated workloads): capacity planning/quotas, cluster or managed compute configuration, distributed training concepts, performance tuning, and chargeback/showback in cloud environments. As a condition of employment, the final candidate who accepts an offer of employment will be required to disclose if they have been subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct; or have filed an appeal of a finding of substantiated misconduct with a previous employer.