experience/skills An understanding of the asset management business and/or financial markets Experience with Iceberg, Snowflake is a bonus Experience of gRPC Experience of working in a Scrum team Commitment to Diversity, Equity, and Inclusion: At T. Rowe Price, our associates are our greatest asset. We thrive More ❯
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 ❯
and CSS. Backend engineering experience with one or more languages (e.g., Node.js, Go, Python, Java, etc.), and familiarity with API design (REST, GraphQL, or gRPC). Frontend state management experience using tools such as Redux, Zustand, or Context API. Strong grasp of component-driven development , accessibility, responsive design, and UI More ❯
and distributed messaging technologies ( Kafka ) Preferred qualifications, capabilities, and skills Hands on experience another statically compiled language like Golang, Rust or C++ Experience with gRPC and Google Protocol Buffers Experience with caching technologies, e.g. Redis Experience with infrastructure as code software, e.g. Terraform Experience using and designing schemas/data 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 ❯
