Intermediate Application Developer

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Job Summary
Fiche de poste :
This position participates in the design, build, test, and delivery of Machine Learning (ML) models and software components that solve challenging business problems for the organization, working in collaboration with the Business, Product, Architecture, Engineering, and Data Science teams. This position engages in assessment and analysis of data sources of structured and unstructured data (internal and external) to uncover opportunities for ML and Artificial Intelligence (AI) automation, predictive methods, and quantitative modeling across the organization. This position establishes and configures scalable and cost-effective end to end solution design pattern components to support prediction model transactions. This position designs trials and tests to measure the success of software and systems, and works with teams, or individually, to implement ML/AI models for production scale.

Responsibilities
The MLOPS developer works on maintaining existing models that are supporting applications such as the digital insurance application and claims recommendation engine. They will be responsible for setting up cloud monitoring jobs, performing quality assurance and testing for edge cases to ensure the ML product works within the application. They are also going to need to be on call on weekends to bring the application back online in case of failure.

• Studies and transforms data science prototypes into ML systems using appropriate datasets and data representation models.
• Researches and implements appropriate ML algorithms and tools that creates new systems and processes powered with ML and AI tools and techniques according to business requirements
• Collaborates with others to deliver ML products and systems for the organization.
• Designs workflows and analysis tools to streamline the development of new ML models at scale.
• Creates and evolves ML models and software that enable state-of-the-art intelligent systems using best practices in all aspects of engineering and modelling lifecycles.
• Extends existing ML libraries and frameworks with the developments in the Data Science and Machine Learning field.
• Establishes, configures, and supports scalable Cloud components that serve prediction model transactions
• Integrates data from authoritative internal and external sources to form the foundation of a new Data Product that would deliver insights that supports business outcomes necessary for ML systems.

Qualifications
Requirements:

• Ability to code in python/spark with enough knowledge of apache to build apache beam jobs in dataproc to build data transfer jobs.
• Experience designing and building data-intensive solutions using distributed computing within a multi-line business environment.
• Familiarity in Machine Learning and Artificial Intelligence frameworks (i.e., Keras, PyTorch), libraries (i.e., scikit-learn), and tools and Cloud-AI technologies that aids in streamlining the development of Machine Learning or AI systems.
• Experience in establishing and configuring scalable and cost-effective end to end solution design pattern components to support the serving of batch and live streaming prediction model transactions
• Possesses creative and critical thinking skills.
• Experience in developing Machine Learning models such as: Classification/Regression Models, NLP models, and Deep Learning models; with a focus on productionizing those models into product features.
• Experience with scalable data processing, feature development, and model optimization.
• Solid understanding of statistics such as forecasting, time series, hypothesis testing, classification, clustering or regression analysis, and how to apply that knowledge in understanding and evaluating Machine Learning models.
• Knowledgeable in software development lifecycle (SDLM), Agile development practices and cloud technology infrastructures and patterns related to product development
• Advanced math skills in Linear Algebra, Bayesian Statistics, Group Theory.
• Works collaboratively, both in a technical and cross-functional context.
• Strong written and verbal communication.
• Bachelors' (BS/BA) degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience.

Type De Contrat
en CDI

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