Specialized training received and work experience gained in the course of service can lead to valuable credentialing and occupational opportunities in related fields in the civilian world, such as IT and network support, computer programming, web development, and information security. A high-school diploma or equivalent is required to become an Enlisted Sailor and an Information Systems Technician. Serving part-time as a Navy Reserve Sailor, your duties will be carried out during your scheduled drilling and training periods. For current or former military Enlisted servicemembers, prior experience satisfies the initial Recruit Training requirement, so you will not need to go through Boot Camp again. For those without prior military experience, you will need to meet the initial Recruit Training requirement by attending Boot Camp in Great Lakes, IL. This training course will prepare you for service in the Navy Reserve and count as your first Annual Training.
As a Cloud Engineer within the AWS AI/ML platform team, you will have the opportunity to work one-on-one with application and infrastructure developers to build and enhance the AI/ML infrastructure and application patterns that power mission-critical applications, ensuring that they’re engineered for high availability, durability, and resiliency. Partner with the Site Reliability Engineering (SRE) team to conduct post-incident reviews and root cause analysis and building monitoring and automation to prevent future incidents. Experience with infrastructure automation tools such as Puppet, Ansible, CloudFormation, or Terraform. Working knowledge of pipeline-automation tools such as Jenkins, CodePipeline, Azure DevOps, or other comparable tools. Prior experience within a DevOps, DevSecOps, SRE, or UNIX/Linux Sys-Admin teams.
We are seeking a Product Analyst to join the CX Mobile and Infrastructure Agile Release Trains (ART).. The ideal candidate will have strong Azure DevOps expertise, experience in the utilities industry, and the ability to guide stakeholders and vendors through backlog priorities and delivery expectations.. Manage and refine the mobile product backlog in Azure DevOps.. Support functional testing, UAT, and release readiness activities.. Azure DevOps (ADO) expertise – backlog management, Agile tracking.
Title: Scrum Master II (Agile Delivery Lead). Key skills: Agile Project Management, Project Management principles of Risk Management, Dependency Planning.. Experience with Jira, Service Now, DevOps toolsets.. Monitor & communicate progress throughout the sprint utilizing metrics on progresstowards completion of sprint goal. Measurement - track and communicate these metrics and utilize these to coach theteam
The AI Cyber Security Engineer for GPS Operations lead the effort to model, configure, test, and integrate AI solutions to deliver world class cyber security services and capabilities.. The AI Cyber Security Engineer role involves adhering to best practices and industry standards from Cyber Security, Software Engineering, DevOps, MLOps, and LLMOps.. The AI Cyber Security Engineer role requires a unique blend of AI technical expertise and the ability to deliver or enhance Cyber Security to Deloitte and our clients.. The team leads a strategic cyber risk program that adapts to a rapidly changing threat landscape, changes in business strategies, risks, and vulnerabilities.. Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect.
Broad technical foundation in software engineering and deep learning (through self-study, practical experience, or PhD).. ML: knows the theory and applied side of deep learning, especially computer vision and foundational model architectures (PyTorch, OpenCV, Tensorflow).. Backend: experience designing and building scalable and high availability server-side systems (Python, NodeJS, or Golang).. Cloud: familiarity with DevOps and infrastructure (i.e. Docker, Kubernetes, GPU scheduling) on cloud (i.e. GCP, AWS).. Monitoring: experience with logging systems and monitoring tools such as DataDog, Sentry, and/or CloudWatch