Machine Learning Engineer for Global Agro Full-time

Overview

As part of the Analytics stream of the Global Agro Digital Transformation initiative, this role will support the repository management and the deployment of analytics use cases for Agro, mostly in Azure environment, including modeling of agricultural processes (crop modeling, storage modeling).

Will be in charge of generating and maintain repositories, production pipelines and APIs, in order to provide recommendations to maximize the agricultural production of supplies in a sustainable way and with good quality.

Responsibilities
• Partner with data scientist working on discovery, prototypes and pilot. Focus on experiment tracking.
• Work with data engineers regarding data pipelines and data versioning.
• Adopt the best MLOps standards to design and develop scalable end-to-end machine learning pipelines.
• Build and deploy machine learning models that run in cloud pipelines.
• Help drive optimization, testing, and tooling to improve quality (unit tests).
• Monitor model performance.
• Support large-scale experimentation done by data scientists.
• Create documentation for insights and knowledge transfer.
• Create and audit reusable packages or libraries.

Qualifications

• Experience with MLOps framework design and building machine learning pipelines.
• Familiarity using cloud platforms (e.g. Azure, AWS or GCP) to serve machine learning models in production.
• Knowledge of code development best practices and tooling (e.g. version control, test-driven development, CI/CD)
• ETL and/or data wrangling techniques.
• Statistical/ML techniques to solve supervised (regression, classification) and unsupervised problems.
• Proficiency in Python development.
• Knowledge of containerization technology (Docker/Kubernetes) and REST API design.
• Business storytelling and communicating data insights in business consumable format.
• Agile methodology for team work and analytics ‘product’ creation.
• Experience using geo-spatial datasets is a plus.
• Experience with computer vision is a plus.

Differentiating Competencies Required:

• Working in a global project, with most connections and meetings being virtual
• Presentation and communication skills
• Assertivity to interact with collegues with different cultural and technical skills
• Flexibility to eventually attend some calls at different timezones
• Interest to learn about Agro and sustainability.
• Ability to attend field visits if needed.