Hybrid
Full-Time
India
About the Role
Job Title: AI Cloud Platform Engineer
Role purpose:
Our strategy revolves around three core pillars: Customer, Simplicity, and Growth. As we focus on enhancing our internal capabilities in AI, Machine Learning, and Generative AI, the role of an AI Cloud Engineer becomes pivotal. This role will support our technology department in driving innovation, improving customer experiences, and simplifying our operations through advanced AI solutions.
The AI Cloud Platform Engineer will be responsible for designing, developing, and deploying AI solutions on cloud platforms. This role involves collaborating with cross-functional teams to integrate AI capabilities into existing systems, creating scalable, efficient, and secure AI infrastructure. The AI Cloud Engineer will play a crucial role in driving innovation and enhancing Vodafone's data-driven decision-making processes.
Core Competencies, Knowledge, and Experience:
Cloud Platforms: Strong experience with Google Cloud Platform (GCP) (preferred), AWS, or Azure.
Programming & Scripting Languages: Proficiency in programming languages such as Python, Java, NodeJS ,shell scripting
AI and Machine Learning: In-depth knowledge of AI and machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch).
Infrastructure as Code: Proficiency in Infrastructure as Code, specifically Terraform (must).
DevOps Practices: Understanding of DevOps practices and tools for continuous integration and deployment.
Data Security: Strong understanding of data security, privacy, and compliance standards.
Preferred Experience: 7+ years of relevant experience in platform engineering, DevOps, or related fields.
Key Responsibilities:
Infrastructure Design and Maintenance: Design, build, and maintain scalable, secure, and high-availability infrastructure on the GCP Cloud platform.
Infrastructure as Code: Manage infrastructure as code using tools like Terraform. Design and develop Terraform modules, templates, and scripts to provision and manage GCP infrastructure resources at an enterprise level.
CI/CD Pipelines: Develop and optimize CI/CD pipelines to ensure smooth, efficient, and reliable data workload releases.
DevOps Engineering: As part of our Platform team, work on various components as a DevOps Engineer.
Automation and Efficiency: Enable the adoption of practices such as SRE and DevSecOps to minimize toil and manual tasks, increase automation, and improve stability. Automate repetitive tasks and processes to improve efficiency and reduce errors.
Scripting and Monitoring: Develop and maintain scripts for infrastructure automation, monitoring, and deployment.
Security Best Practices: Implement and enforce security best practices across the infrastructure and deployment processes.
AI/ML Integration: Collaborate with AI/ML teams to integrate machine learning models and algorithms into the cloud infrastructure.
Data Services: Utilize GCP Data Services such as BigQuery, Dataproc, and Composer for data processing and analysis.
Skills and Experience:
Cloud Platforms: Google Cloud (preferred), AWS, or Azure.
Terraform Modules: Able to design and develop Terraform modules, templates, and scripts to provision and manage GCP infrastructure resources at an enterprise level.
Security Management: Usage of enterprise Security Management solutions including GCP Secret Manager.
Networking: Network fundamentals – Firewalls and ingress/egress patterns.
GCP Services: GCP Networking, GCP Data Services, GCP Serverless.
Security: Experience with GCP-based security hardening, including IAM, ACL, firewall rules.
CI/CD Tools: Experience with CI/CD tools. GitHub Actions – CI/CD experience would be a plus.
Containerization: Experience with Docker/Kubernetes (creating images, deployment).
GCP Data Services: Knowledge about GCP Data Services would be a plus, such as BigQuery, Dataproc, Composer.
AI/ML Frameworks: Experience with AI/ML frameworks such as TensorFlow and PyTorch.
Non-Technical Skills:
Problem-Solving: Excellent problem-solving and analytical skills.
Communication: Strong communication and collaboration abilities.
Adaptability: Ability to work in a fast-paced, dynamic environment.
Organizational Skills: Strong organizational and time management skills.
Mindset: A proactive and innovative mindset.
Role purpose:
Our strategy revolves around three core pillars: Customer, Simplicity, and Growth. As we focus on enhancing our internal capabilities in AI, Machine Learning, and Generative AI, the role of an AI Cloud Engineer becomes pivotal. This role will support our technology department in driving innovation, improving customer experiences, and simplifying our operations through advanced AI solutions.
The AI Cloud Platform Engineer will be responsible for designing, developing, and deploying AI solutions on cloud platforms. This role involves collaborating with cross-functional teams to integrate AI capabilities into existing systems, creating scalable, efficient, and secure AI infrastructure. The AI Cloud Engineer will play a crucial role in driving innovation and enhancing Vodafone's data-driven decision-making processes.
Core Competencies, Knowledge, and Experience:
Cloud Platforms: Strong experience with Google Cloud Platform (GCP) (preferred), AWS, or Azure.
Programming & Scripting Languages: Proficiency in programming languages such as Python, Java, NodeJS ,shell scripting
AI and Machine Learning: In-depth knowledge of AI and machine learning algorithms and frameworks (e.g., TensorFlow, PyTorch).
Infrastructure as Code: Proficiency in Infrastructure as Code, specifically Terraform (must).
DevOps Practices: Understanding of DevOps practices and tools for continuous integration and deployment.
Data Security: Strong understanding of data security, privacy, and compliance standards.
Preferred Experience: 7+ years of relevant experience in platform engineering, DevOps, or related fields.
Key Responsibilities:
Infrastructure Design and Maintenance: Design, build, and maintain scalable, secure, and high-availability infrastructure on the GCP Cloud platform.
Infrastructure as Code: Manage infrastructure as code using tools like Terraform. Design and develop Terraform modules, templates, and scripts to provision and manage GCP infrastructure resources at an enterprise level.
CI/CD Pipelines: Develop and optimize CI/CD pipelines to ensure smooth, efficient, and reliable data workload releases.
DevOps Engineering: As part of our Platform team, work on various components as a DevOps Engineer.
Automation and Efficiency: Enable the adoption of practices such as SRE and DevSecOps to minimize toil and manual tasks, increase automation, and improve stability. Automate repetitive tasks and processes to improve efficiency and reduce errors.
Scripting and Monitoring: Develop and maintain scripts for infrastructure automation, monitoring, and deployment.
Security Best Practices: Implement and enforce security best practices across the infrastructure and deployment processes.
AI/ML Integration: Collaborate with AI/ML teams to integrate machine learning models and algorithms into the cloud infrastructure.
Data Services: Utilize GCP Data Services such as BigQuery, Dataproc, and Composer for data processing and analysis.
Skills and Experience:
Cloud Platforms: Google Cloud (preferred), AWS, or Azure.
Terraform Modules: Able to design and develop Terraform modules, templates, and scripts to provision and manage GCP infrastructure resources at an enterprise level.
Security Management: Usage of enterprise Security Management solutions including GCP Secret Manager.
Networking: Network fundamentals – Firewalls and ingress/egress patterns.
GCP Services: GCP Networking, GCP Data Services, GCP Serverless.
Security: Experience with GCP-based security hardening, including IAM, ACL, firewall rules.
CI/CD Tools: Experience with CI/CD tools. GitHub Actions – CI/CD experience would be a plus.
Containerization: Experience with Docker/Kubernetes (creating images, deployment).
GCP Data Services: Knowledge about GCP Data Services would be a plus, such as BigQuery, Dataproc, Composer.
AI/ML Frameworks: Experience with AI/ML frameworks such as TensorFlow and PyTorch.
Non-Technical Skills:
Problem-Solving: Excellent problem-solving and analytical skills.
Communication: Strong communication and collaboration abilities.
Adaptability: Ability to work in a fast-paced, dynamic environment.
Organizational Skills: Strong organizational and time management skills.
Mindset: A proactive and innovative mindset.