Machine Learning Engineer - London, United Kingdom - Jefferson Frank

Jefferson Frank

Verified Company

London, United Kingdom

3 weeks ago

Posted by:

Tom O´Connor

beBee Recruiter

Description

Machine Learning Engineer / MLOps - FTC - 12 months - £75,000

A core figure within the travel infrastructure of London are looking to increase their ML team by adding a key member to perform a specialist function within the unit.

We are looking for an experience Machine Learning Engineer/MLOps to join our client on a 12 month fixed term contract.

You will be focused around the manipulating, cleansing and transformation of data to make sure it is "Machine Learning Ready".

Job Responsibilities: _
Leading on data engineering and ETL tasks which will exploit the AWS machine learning stack to examine novel machine learning problems within the rail industry.
Working closely with data scientists by preparing data for them to develop statistical algorithms to implement solutions to key business challenges and advising on the best approach to do this.
Develop best practices for ML Ops, engineering tasks, code development, code deployment, ethics, and approach to productionising solutions.
Provide quality assurance of engineering tasks by code checking and any other practices necessary
Conducting feasibility and practicality testing of business challengeled machine learning ideas, to help strengthen the data science portfolio.
Mapping out data feeds and systems in collaboration with Solutions Architects and the IT Team, to then build richer pools of data for proof of concept/production solution design and build.
Job Requirements: _
A proven track record of creating and designing data pipelines, exposing and linking data from multiple systems
Experience with security and monitoring best practice, preferably using AWS Cloud infrastructure
A good understanding of coding best practices and experience with code and data versioning (using Git/CodeCommit), code quality and optimisation, error handling, logging, monitoring, validation and alerting.
Experience of iteratively making data 'machine learning ready' preferably within AWS machine learning stack (primarily SageMaker)
Fluent in writing well tested, readable code using Python that is capable of processing large volumes of data.
An excellent command of the basic libraries for data science (e.g. NumPy, Pandas)
Experience in writing complex queries to gather insight against relational and nonrelational data sources.
Technical experience of mapping out data feeds to integrate and separate data to produce, transform and test new machine learning ideas