have proven technical skills in at least one (ideally several) of the following: • Containerisation/Container Security; • Microservice Architectures; • API Development, REST, Swagger, OpenAPI, gRPC; • Cloud platforms and technologies, AWS, (e.g. Lambda, API Gateway, EKS), Azure, Google Cloud Platform (GCP); • Cloud native Apps, Kubernetes, OpenShift, MicroK8s; • Infrastructure as Code (IaC More ❯
versioning, packaging, and delivering. Good knowledge of SQL, relational databases like PostgreSQL Technical Skills nice to have: Kotlin, Typescript Angular GitHub Action pipelines REST, gRPC Elastic Stack, Prometheus, Grafana Additional Information Evooq is a global provider of technology-driven solutions for wealth managers. We are building an ecosystem that combines More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
unsupervised, reinforcement learning). Experience building and maintaining ML pipelines and data pipelines. Proficiency in model deployment techniques (e.g., serving models with REST APIs, gRPC, or via cloud services). Hands-on experience with cloud platforms (AWS, GCP, Azure) for model training and deployment. Deep understanding of MLOps concepts: monitoring More ❯
