ML Ops

FieldBox.ai is hiring!

About

With a unique combination of expertise in data science, software/IT and industrial engineering, FieldBox.ai helps industrial companies dramatically improve their operational efficiency with artificial intelligence.
As an AI Operator, FieldBox.ai offers a complete range of services, enhanced by latest technologies, to enable the development, deployment, run and scale of AI in industrial operations. FieldBox.ai has known a fast paced and continuous growth, reaching over 80 employees located in three offices worldwide: Bordeaux, Paris and Singapore.

Job Description

As an AI Operator, we value not only building the best models for our clients, but also ensuring that these models behave and perform at their highest potential once operationalized. Automatic deployment and continuous monitoring of the deployed models and the underlying infrastructure powering them is a fundamental prerequisite in order for our clients to confidently take full advantage of the predictive and intelligent models we build for them, develop business practices based on the model results, and, therefore, transform themselves into actual AI-driven businesses.
We seek to achieve consistency, repeatability, and speed in the operationalization of all models. This will enable fine tracking of model usage and performance, and provide better options to meet business requirements and risk mitigation objectives.
If you think that POCs are not enough, and that you strive to bring models to production, if you love not only seeing what kind of value data science can bring, but also being a part of building continuous delivery and automation pipelines for the full model lifecycle: we are looking for a passionate ML Ops contributor to move this strategic initiative forward, bring value, and to rise up to the challenge.

Job description

As ML Ops, you are in charge of responding to project needs in order to maximize impact on client operations, while building a company-wide ML Ops infrastructure, tooling and methods. You stand at the crossroad of several technical teams: Data Scientists, Software Engineers, Data Engineers and DevOps. You work closely with the Head of ML Ops and the Head of Engineering and Data Science.

Your missions:

Deploying:

  • Develop and maintain automated pipelines for model training and consistent deployment.

Tracking:

  • Monitor models and their generated outputs continuously;
  • Monitor data availability, quality;
  • Ensure relevance of model results via monitoring of input data, model output and performance.

Fixing:

  • Manage incidents in a timely fashion. Diagnose root cause, and take action to recover service level.

Capitalizing:

  • Facilitate capitalization with feature stores, model catalogs, reporting dashboards;
  • Document and leverage past achievements and performance.

Building:

  • Develop robust solutions to improve overall model deployment and monitoring tooling;
  • Anticipate and respond to projects’ needs;
  • Leverage relevant open source technologies to accelerate project delivery and contribute to the development of our monitoring and observability infrastructure.

Scouting:

  • Watch, screen and evaluate the latest technologies used by the ML Ops community and facilitate their usage at Fieldbox.

Preferred Experience

Your profile

  • Master degree, or equivalent;
  • 5+ years of experience with software engineering and machine learning;
  • Programming proficiency in scripting languages like Python (mandatory) and Go (nice to have);
  • Knowledge of back-end technologies, in particular SQL and NoSQL databases (such as Postgresql, Redis);
  • Knowledge of typical architectures of data-related projects (dealing with big data and real-time consumption) and associated tools such as Pyspark, Kafka;
  • Fine understanding of Data Science model lifecycle: data collection, model training, model deployment, ideally with a good range of libraries for modeling;
  • Good experience with Linux as operating system;
  • Good experience with version control and CI/CD, Docker, Kubernetes;
  • Fluent in English.

In a sentence: you master the toolset to enable a model to run seamlessly in production.

We are also looking for the following skills:

  • Autonomous and self-driven, you are able to tackle Data Science and ML Ops tooling questions;
  • Outstanding analytical and problem solving skills;
  • You love to communicate complex ideas effectively, both in French and English, internally and externally;
  • Finally, you enjoy analyzing and solving model-related incidents in a timely and resilient fashion.

Nice to have:

  • ML Ops previous experience;
  • Demonstrated technical leadership in a similar position;
  • Experience with commercial ML Ops stack such as Azure ML, AWS Sagemaker, Google Vertex;
  • Experience with web application frameworks such as Django;
  • Experience with Computer Vision, Time Series Forecasting.

Compensation

  • Competitive package based on your profile and experience;
  • You will work in a quiet and pleasant environment, with breathtaking views of the Bassins à Flot. Our offices are located in the center of Bordeaux, close to the Cité du Vin;
  • You are joining a company where everyone is strongly focused, keeps scientific truth as a compass of his/her action, and cares about his/her personal balance as much as that of his/her coworkers.

If you are interested in the project, apply online and tell us why we should hire you.

Additional Information

  • Contract Type: Full-Time
  • Location: Bordeaux, France (33000)
  • Education Level: Master's Degree
  • Experience: > 5 years