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GCP Architect - Docker/Kubernetes (10-15 yrs)

10-15 years

GCP Architect - Docker/Kubernetes (10-15 yrs)

TekPillar

posted 4d ago

Job Role Insights

Flexible timing

Job Description

Job Title : GCP Architect

Location : Bangalore

Experience : 4-6 years

About the Role :

We are looking for a skilled Embedded Systems & Cloud Engineer with expertise in GCP, Python, Docker, and Kubernetes to join our dynamic team. This role requires a strong foundation in embedded software development while bridging the gap between cloud-based solutions and edge computing. If you are passionate about scalable deployments, automation, and cloud-integrated embedded systems, this is the perfect opportunity for you.

Key Responsibilities :

- Develop, deploy, and manage cloud-integrated embedded applications using GCP services.


- Design and maintain Docker containers and Kubernetes clusters for scalable embedded solutions.


- Work with Python-based frameworks to automate cloud and on-premise embedded system testing.


- Implement microservices architecture for seamless cloud-embedded system integration.


- Collaborate with cross-functional teams to optimize CI/CD pipelines and improve deployment efficiency.


- Troubleshoot and debug cloud and embedded system connectivity issues.


- Ensure security best practices in containerized and cloud environments.




Must-Have Skills :


- Strong experience with Google Cloud Platform (GCP) - Compute Engine, Pub/Sub, IoT Core, Cloud Functions.


- Hands-on experience in Docker (creating and managing containers) & Kubernetes (deployments, Helm charts, scaling).


- Proficiency in Python for scripting, automation, and cloud service interactions.


- Experience in embedded systems development with a strong understanding of Base SW, CAN, SPI, UART, I2C, and LIN.


- Knowledge of DevOps, CI/CD pipelines, and test automation frameworks.


Good-to-Have Skills :


- Experience with dSPACE HIL, CAPL scripting, and UDS diagnostics.


- Familiarity with Terraform, Ansible, and infrastructure automation.


- Exposure to Machine Learning on Edge devices using TensorFlow Lite.


Functional Areas: Other

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