team will be responsible for the seamless deployment, monitoring, and maintenance of machine learning models in production. Acting as the critical link between the data science and R&D teams, this team will ensure that ML models transition smoothly from development to production, maintaining high availability, scalability, and performance. … deployments to ensure reliability and efficiency. Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management. Collaborating closely with data scientists to understand and implement model requirements. Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems. Implementing … provisioning and deployment using IaC tools. Collaborate with team leader to define technical strategies. Requirements: 4+ years of experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency More ❯
deployments to ensure reliability and efficiency. Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management. Collaborating closely with data scientists to understand and implement model requirements. Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems. Implementing … best practices in security, cost management, and infrastructure design for cloud environments. The Ideal Candidate: 4+ years of experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency … with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. Knowledge of dataengineering concepts (e.g., ETL, data pipelines). Ability to troubleshoot complex production systems.. If you have the skills and desire for this, then please email your More ❯
deployments to ensure reliability and efficiency. Continuously improving the architecture, processes, and tools used for model deployment, monitoring, and lifecycle management. Collaborating closely with data scientists to understand and implement model requirements. Partnering with R&D teams to align technical strategies and integrate ML solutions into broader systems. Implementing … best practices in security, cost management, and infrastructure design for cloud environments. The Ideal Candidate: 4+ years of experience in MLOps, DevOps, or software engineering roles. Strong programming skills in Python and familiarity with ML frameworks. Extensive experience with AWS services (e.g., SageMaker, ECS, Lambda) and cloud environments. Proficiency … with containerization and orchestration tools (Docker, Kubernetes). Experience with version control systems and CI/CD pipelines. Knowledge of dataengineering concepts (e.g., ETL, data pipelines). Ability to troubleshoot complex production systems.. If you have the skills and desire for this, then please email your More ❯
