▷ (Apply Now) Engineer, Software - Platform Automation [T500-20372]

ANSR is hiring for one of its clients.

About T-Mobile:

T-Mobile US, Inc. (NASDAQ: TMUS), headquartered in Bellevue, Washington, is America’s supercharged Un-carrier, connecting millions through its strong nationwide network and flagship brands, T-Mobile and Metro by T-Mobile. Customers benefit from an unmatched combination of value, quality, and exceptional service experience.

About TMUS Global Solutions:

TMUS Global Solutions is a world-class technology powerhouse accelerating the company’s global digital transformation. With a culture built on growth, inclusivity, and global collaboration, the teams here drive innovation at scale, powered by bold thinking.

TMUS India Private Limited is a subsidiary of T-Mobile US, Inc. and operates as TMUS Global Solutions.

About the Role:

The Engineer – you will be a key member of the CFL Platform Engineering and Operations team . Platform Automation will focus on building, maintaining, and scaling automation solutions for cloud-native platforms and infrastructure. This role is essential to supporting high-velocity development teams through reliable, scalable, and self-service platform capabilities. You will work across provisioning, CI/CD, observability, and reliability layers to reduce operational overhead and drive consistency through code. The ideal candidate is hands-on, collaborative, and passionate about infrastructure automation, DevOps practices, and developer experience.

What You’ll Do:

- Develop and maintain scripts, tools, and workflows for provisioning, deployment, patching, and configuration management
- Build internal developer tools and automation that streamline infrastructure and application lifecycle management
- Design and implement Infrastructure-as-Code (IaC) using Terraform, Helm, or Ansible
- Contribute to event-driven automation using CI/CD tools such as GitLab, Jenkins, or ArgoCD
- Integrate monitoring, alerting, and logging capabilities into platform automation using tools like Prometheus, Grafana, or Splunk
- Develop and maintain auto-remediation and health check mechanisms to support resilient operations
- Apply GitOps principles to infrastructure and application delivery
- Collaborate with DevOps, cloud, SRE, and security teams to gather requirements and build reliable, reusable automation components
- Create and maintain documentation, usage guides, and onboarding material for internal automation tools
- Participate in daily standups, retrospectives, and engineering planning sessions

What You’ll Bring:

- Bachelor’s degree in Computer Science, Engineering, or a related technical field
- 2-5 years of experience in DevOps, platform, automation, or infrastructure engineering roles
- Hands-on experience in scripting and automation using Python, Bash, or Go
- Proficiency in Infrastructure-as-Code tools such as Terraform, Ansible, or Helm
- Familiarity with CI/CD pipelines and tools like GitLab CI/CD, Jenkins, or ArgoCD
- Working knowledge of Kubernetes and container-based architecture
- Experience working in cloud environments, preferably Azure
- Exposure to building internal tooling, developer self-service portals, or onboarding automation
- Familiarity with observability practices and tooling for metrics and logging
- Understanding of platform security practices, secrets management, and API integrations
- Effective communication and cross-functional collaboration skills
- Strong problem-solving ability and a drive to automate manual or repetitive work
- Willingness to learn new tools and technologies and contribute to team-wide improvement initiatives

Must Have Skills:

- Application & Microservice: Java, Spring boot, API & Service Design
- Any CI/CD Tools : Gitlab Pipeline/Test Automation/GitHub Actions/ Jenkins /Circle CI
- App Platform: Docker & Containers (Kubernetes)
- Any Databases : SQL & NOSQL (Cassandra/Oracle/Snowflake/MongoDB)
- Any Messaging: Kafka, Rabbit MQ
- Any Observability/Monitoring: Splunk/ Grafana/ Open Telemetry /ELK Stack/ Datadog/ New Relic/ Prometheus)
- AI/Machine learning: Anomaly detection

Nice To Have:

- Baseline anomaly detection for logs/metrics
- Configure AIOps (Datadog, Moogsoft, BigPanda)
- Kafka/EventHubs streaming ingestion
- OTel collector pipelines (enrichment, normalization)