Machine Learning Engineer

Key Skills

Java Programming, Python Programming, Django, Framework, SQL, Git, API, Machine learning techniques, Data Visualization, Docker, Kubernetes-K8s, Continuous Integration & Continuous Delivery - CI/CD, PySpark, Flask, Databricks, Data Lake, Data pipelines, Data Analysis

Job Description

• Strong knowledge on Azure Machine Learning Service, Databricks, Microservices.
• Excellent hands on coding skills on Python/Pyspark.
• Hands-on experience in Azure datala lake, Blob storage, SQL etc.
• Expert in Azure Kubernetes Services
• Good Knowledge in Azure Cloud.
• Experience in creating the RESTful services in python using Flask/Django or any similar framework
• Experience in working on any API Management tool like APIGEE and API security protocols.
• Experience in ML platforms such as Databricks and Azure Machine Learning Services
• Experience of deploying application and services at scale on azure public cloud ( highly desirable)
• Hands-on experience in Docker, Kubernetes.
• Experience in implementing CI/CD pipelines in ML solutions.
• Excellent experience with source code management tools (git)
• Always ready to learn more and adopt new cutting edge technology with right value proposition.

KEY EXPECTED ACHIEVEMENTS

• Business understanding & Solution implementation
• The business need is understood and formalized in a descriptive datasheet or specifications
• The methods are identified through external researches and selected by their theoretical bases, advantages and drawbacks.
• The results of the analysis or the models are presented to the customers with data visualisation tools indicating the performances and the limits. The source code is delivered and explained if necessary.

Quality/Capitalization

• All stages of Machine learning project are implemented (pipeline, components, tests), respecting quality standards
• All valuable code is re-usabled by community
• Data Analysis models are monitored (technological watch) and partnerships are developed

Health of the metier

• The metier is well supported by the data scientist network with proposals for improvements to develop people, technos, processes.