What are the responsibilities and job description for the US E-Consulting Services - DevOps Engineer - Project Delivery Senior Analyst position at Deloitte US?
DevOps Engineer - Project Delivery Senior Analyst
Are you an experienced, passionate pioneer in technology who wants to work in a collaborative environment? As an experienced DevOps Engineer you will have the ability to share new ideas and collaborate on projects as a consultant without the extensive demands of travel. If so, consider an opportunity with Deloitte under our Project Delivery Talent Model. Project Delivery Model (PDM) is a talent model that is tailored specifically for long-term, onsite client service delivery.
Work you’ll do/Responsibilities
- Build, maintain, and improve CI/CD pipelines (Tekton) to support consistent build, test, and deployment of data science/ML workloads.
- Containerize applications and model services using Docker; manage images, registries, and runtime configuration.
- Deploy and operate workloads on Kubernetes (jobs/services/ingress, configmaps/secrets, scaling) across dev/test/prod environments.
- Troubleshoot Linux, container, and cluster issues end-to-end; perform root-cause analysis and implement durable fixes.
- Partner with data scientists and engineers to productionize analytics/ML solutions with reliable runtime, monitoring, and repeatable releases.
- Implement basic operational standards (logging/metrics, alerting, runbooks, access controls) to improve reliability and supportability.
- Automate routine platform tasks using scripting and standard tooling; continuously reduce manual effort.
- Document solutions and share knowledge; proactively “navigate for solutions” when requirements or failures are ambiguous.
- Documentation & Knowledge Sharing: Create and maintain comprehensive documentation for cloud architectures, configurations, and operational procedures.
- Independently and collaboratively lead client engagement workstreams focused on improvement, optimization, and transformation of processes including implementing leading practice workflows, addressing deficits in quality, and driving operational outcomes
The Team
AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.
Our AI & Engineering - Industry Solutions teams works with the Customer group to bring a flexible capability and fluid capacity model to the delivery of small technology projects and enhancements.
Required Qualifications
- 3 yrs Hands-on experience with Linux administration and troubleshooting (process, networking basics, permissions, services).
- 3 yrs Docker experience (image builds, debugging, registries, best practices).
- 3 yrs Kubernetes experience (deployments/jobs, services, ingress, configmaps/secrets, troubleshooting and scaling).
- Experience building or operating Tekton pipelines/tasks (or closely related Kubernetes-native CI/CD).
- Demonstrated ability to independently investigate issues, synthesize options, and drive solutions to closure.
- Experience with Git and standard SDLC practices (tickets, code review participation, documentation)
- Bachelor's degree, preferably in Computer Science, Information Technology, Computer Engineering, or related IT discipline; or equivalent experience
- Limited immigration sponsorship may be available
- Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve
Preferred Qualifications
- Exposure to ML/data workloads in Kubernetes (batch jobs, model serving, feature/data pipelines).
- Familiarity with Helm/Kustomize, GitOps (Argo CD/Flux), and infrastructure automation (Terraform/Ansible).
- Observability tooling experience (Prometheus/Grafana, ELK/OpenSearch) and incident response practices.
- Scripting in Bash/Python for automation and tooling.