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)