Experience & qualifications :

- Practical experience in the field of Machine Learning with experience of deploying models in a production environment (knowledge of modern pipeline frameworks like Kubeflow/MLflow/TFX is a plus)

- Solid knowledge in at least one programming language (Python, Java, C++)

- Understanding and experience in Software Engineering concepts & best practices

- Proven experience with machine learning offerings in the Google Cloud Platform. Cloud certifications (around Azure/AWS/GCP is a plus).

- Demonstrated ability to work with cross-functional IT/Data Science teams in a highly innovative and fast-paced environment.

Additional (Good to have) Skills :

- Knowledge of Version Control, CI/CD, automation, virtualization using containers and their orchestration (e.g. Git, Jenkins, Docker, Kubernetes etc.)

- Practical insights into the use of modern data architectures or databases (e.g. Oracle, Teradata, Sybase, MS SQL), Big Data (Hadoop, Graph DB etc.) up to BI and visualization tools (Tableau, PowerBI, Looker).

- Familiarity with contemporary Google Cloud Architectures, Virtualization and Containerization methods, tools, and techniques on GCP

- Familiarity with Cloud security, networking topics is a plus

- Familiarity with modern ML platforms like H20, Databricks, Dataiku is a plus

Job description :

As a Google Cloud Platform (GCP) ML Engineer you are an expert engineer with an eye for AI. You will be required to develop a holistic understanding of the AI/ML solution you are building, including transferring some of the software engineering best practices to the data science world.

Having a deep understanding of the mathematical underpinnings of the Machine Learning algorithms is a must have, as you will be required to know what algorithms are available and when and how to apply them.

Roles & Responsibilities :

- Use your analytical mind to develop end-to-end innovative and intelligent solutions for our customers covering everything from business understanding to model development, validation & deployment

- Be an Agile learner with ability to adapt to new sets of emerging tools & technologies including proprietary solutions & methodologies

- As part of global team working in collaboration with Data Engineers and Data Scientists, ensure that ML models and pipelines are successfully implemented in real productive environments

- Work in close contact with the decision-makers on the customer side, including prototyping and demonstrating solutions in customer environments.

- Know how to address technical problem solutions and implement them in practice with the help of native tools on Google Cloud Platform

- Ability to make decisions and take responsibility for projects and tasks

- Design, implement, test, and productionize the models on Google Cloud Platform (GCP) using GCP native resources.

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