Machine Learning Engineer

We are looking for a Machine Learning Engineer with extra experience running and scaling models on Public Cloud to join our team and work with our engineering team to optimize, implement, and maintain our organization’s cloud-based systems.
A Machine Learning Engineer’s responsibilities include deploying and debugging models, as well as executing new cloud initiatives. 
Ultimately, you will work with different IT professionals and teams to ensure our cloud computing systems meet the needs of our organization and customers
Your key responsibilities
Design, develop, and deploy modular models on cloud-based systems
Develop and maintain cloud solutions in accordance with best practices
Ensure efficient functioning of data storage and process functions in accordance with company security policies and best practices in cloud security
Identify, analyze, and resolve infrastructure vulnerabilities and application deployment issues
Regularly review existing models and systems and make recommendations for improvements
Collaborating with engineering and development teams to evaluate and identify optimal cloud solutions
Modifying and improving existing systems
Educating teams on the implementation of new cloud technologies and initiatives
Technical skills
Proven work experience as a Machine Learning Engineer or similar role
GCP certifications preferred
Troubleshooting and analytical skills
Strong communication and collaboration skills
Relevant training and/or certifications as a Google Cloud Engineer
Proficiency in Google Compute Engine. Mastering this skill involves creating, managing, and scaling virtual machines (VMs) to meet diverse workload requirements.
Designing a solution architecture on Google Cloud Platform (GCP) skill involves carefully crafting a framework that aligns with the specific needs and objectives of a project.

Design Solution Architecture on GCP includes the interconnected elements like google cloud skills that enable software tracking and virtual network management. This information forms the foundation for defining the architecture objectives, which serve as guiding principles throughout the design process.

The solution architecture should also consider cost optimization, flexibility for future enhancements, and adherence to best practices and industry standards.

A strong grasp of programming languages such as Python, Java, Go, and Node.js is essential as they are widely used in GCP skills and its ecosystem.

Expertise in programming frameworks and libraries specific to GCP skills, such as Google Cloud SDK, Cloud Client Libraries, and Cloud APIs.

Understanding concepts like server less computing, containerization using technologies like Docker and Kubernetes, and event-driven architectures is beneficial for developing scalable and efficient applications on GCP

Knowledge of Containers
Knowledge of Infrastructure Automation Tools
Proficiency in a range of tools, including Ansible, Docker,Windows PowerShell and Linux/Unix is crucial for a GCP Platform Engineer

Expertise in Google Cloud Storage
Familiarity with other GCP services like BigQuery for data analytics, Cloud Pub/Sub for event-driven messaging, and Cloud Functions for server less computing
 
Software Security Skills 
Software security skills in Google Cloud involve expertise in various aspects of securing applications and data on the platform. This includes:
• Understanding and configuring Identity and Access Management (IAM) to control access to resources
• Implementing network security measures such as firewall rules and encryption
• Monitoring and logging security events using tools like Stackdriver Logging, ensuring data encryption at rest and in transit, securing containers and Kubernetes clusters, complying with security standards and regulations, developing incident response plans, automating security checks and vulnerability scanning, and following secure development practices. These skills are crucial for maintaining the confidentiality, integrity, and availability of software systems deployed on Google Cloud and protecting against potential threats and vulnerabilities.
Coding and Scripting Skills
• Proficiency in programming languages like Python, Java, or Go to build and customize applications on the Google Cloud Platform.
• Knowledge of infrastructure-as-code (IaC) tools such as Terraform and/or Google Cloud Deployment Manager enables defining and managing infrastructure resources using code. Automation is key, utilizing scripting languages and tools like Cloud SDK or Cloud APIs to automate tasks, deployments, and resource management.
• Familiarity with containerization technologies like Docker and Kubernetes enables efficient deployment and management of applications. Cloud API integration and utilization of monitoring and logging tools like Stackdriver aid in application monitoring, troubleshooting, and enhancing overall performance. These skills empower developers to optimize and harness the capabilities of Google Cloud for building scalable and reliable cloud solutions.    
Testing Skills
The primary objective of any GCP Engineer is to expedite the software delivery process for clients, ensuring swift and efficient deployment.
To excel GCP Skills, it is imperative to possess:
• Strong testing skills and a comprehensive understanding of the testing process. Testing plays a critical role in driving automation and ensuring successful outcomes in the role.
• Additionally, once the appropriate tests are established, a sense of assurance prevails, knowing that each component functions as intended. Tests can be conducted at various stages, from development to deployment, to ensure seamless integration of new features throughout the entire system.
• Finally, emphasizing quality is paramount in the realm of applications and software. As a result, one should continuously prioritize and conduct rigorous testing to deliver high-quality work.
Continuous Integration and Continuous Deployment (CI/CD)
• Continuous Integration and Continuous Deployment (CI/CD) in Google Cloud refers to a set of practices and tools that enable developers to automate the process of building, testing, and deploying applications. CI involves automatically integrating code changes into a shared repository and running tests to ensure code quality. CD takes it further by automating the deployment of tested code to production environments. In Google Cloud, CI/CD pipelines can be set up using tools like Cloud Build, Cloud Source Repositories, and Cloud Deployment Manager. These pipelines help streamline development workflows, increase collaboration, and ensure rapid and reliable application delivery, allowing teams to deliver software updates more frequently and with reduced manual effort.
• Azure DevOps and Azure Pipelines
 
An ideal candidate should also demonstrate the following behaviours:
• Self-learning abilities
• Radiates energy & enthusiasm for his field, and can instil the same passion in others
• Naturally curious & always a step ahead
• Critical thinker, challenging others in a constructive way, continuously looking for improvements
• Can combine big-picture thinking, with zooming in on details