2 of 2 Deployment Automation Jobs in Tyne and Wear

a top university. Strong ability in scripting with PowerShell or a similar language. Hands-on coding skills in at least one of C#, Java, or Python. Practical knowledge of automation tools such as Terraform or Ansible. Background in container platforms (e.g., Docker) with orchestration using Kubernetes or Swarm. Familiarity with system monitoring solutions (e.g., ELK, Grafana, or similar). … and clients to resolve challenges and deliver solutions. Desirable (but not essential): Working knowledge of Atlassian products such as JIRA or Bitbucket. Exposure to artifact management tools like Artifactory. Deployment automation experience with Octopus Deploy. Awareness of code quality and analysis tools (e.g., SonarQube). Background with job scheduling platforms such as Control-M. Experience with major cloud More ❯

architecture to design and deliver production-grade machine learning pipelines. The ideal candidate will be passionate about bridging the gap between data science experimentation and scalable production systems, driving automation, and enabling faster innovation cycles. Key Responsibilities Architect, build, and maintain production-grade ML Ops pipelines to automate deployment, monitoring, and scaling of machine learning models. Collaborate with … workflows using Python. Leverage AWS cloud services (EC2, S3, Lambda, SageMaker, RDS, DynamoDB, Redshift, etc.) to build robust, scalable, and cost-effective solutions. Apply AIOps practices to enhance monitoring, automation, and resilience of ML systems. Implement best practices in data engineering, version control, CI/CD, and infrastructure as code. Ensure the security, reliability, and compliance of data pipelines … experience as a Senior Data Engineer, MLOps Engineer, or similar role. Strong background in data structures, algorithms, and software engineering principles. Advanced proficiency in Python for data wrangling, pipeline automation, and ML workflows. Expertise in AWS services, including databases (RDS, DynamoDB, Redshift) and machine learning/AI (SageMaker, AI/ML frameworks). Hands-on experience with ML pipeline More ❯