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AWS Engineer- Data sciences support

AXA Belgium
Full-time
On-site
Brussels, Belgium
A nice reward for your hard work

An attractive salary, supplemented with discretionary personal and collective bonuses and of course, meal and eco vouchers.


 

The benefits of working at an insurance company

From group to hospitalization insurance and ambulatory care (family members can also join at an advantageous rate) and with up to 30% discount on all additional insurance products.


 

A job that respects your personal life and dreams

With 35 days off per year to recharge, home working options, sports facilities and professional training to make sure you stay in shape mentally, physically and professionally!


In Belgium, AXA is market leader in non-life insurance. We have more than 3,000 enthusiastic employees whose aim is to move from payer to partner for our 3 million clients.  


 

Our employees are our greatest asset. Therefore, a pleasant and modern working atmosphere is crucial to us. Together we seek to foster a diverse and inclusive culture where thoughts and ideas are valued, respected and appreciated.  


 

With every step we take, we keep our values in mind\: Customer First, Integrity, Courage and One AXA.


As AWS Engineer, you have:



  • Min 2 years’ experience

  • A Master degree in Engineering or IT;

  • An in-depth and practical knowledge of MLOps;

  • Hands-on experience with Python and AWS services;

  • Knowledge of AWS Sagemaker, AWS Lambda, AWS Glue, AWS Step Functions,…

  • Experience in developing production grade end-to-end solutions;

  • Notions of Machine Learning, Statistics and Optimization;

  • Knowledge of French and/or Dutch as well as very good knowledge of English.


And you are also:



  • Outcome-obsessed, pragmatic engineer who is relentlessly focused on creating positive business impact;

  • Eager to learn on the job and benefit from the experience of your colleagues;

  • Capable of identifying issues and proactive to solve them;

  • Excellent communication skills;

  • Team player.


At AXA, we want to be more than the world leader in insurance and asset management. 


 

Our purpose is ‘Act for human progress by protecting what matters’. As an insurance company, we want to watch over every individual, society and the world while always keeping the future in mind.  


 

As an insurer, AXA Belgium is also a key player in the field of prevention. Protection is in our DNA, as evidenced every day by the extensive investments in research and risk awareness.


 

At AXA, we reject unfair or unlawful discrimination in any form. More info in our Diversity & Inclusion Policy.


In AI&Analytics Solutions team, we want to ensure that we’re bringing smart decision making (human augmentation or automated) to the forefront of our processes/journeys by establishing a data and experimentation culture that could ultimately become a competitive advantage in today’s fast-changing world.


Do you want to get involved and build competitive ML Data Product? Are you excited to create business value out of data?


As a member of the AI&Analytics Solutions team, you aim at elaborating solutions using technologies in the fields of Analytics and AI (ML). You will coach and technically guide, under the supervision of the team tech lead, the Data Scientists on all levels of their technical skills evolution. Your role will be instrumental in shaping and maintaining the team AWS accounts. You will provide support for the industrialisation of models and their deployment into production applying best practices enabling the evolution of code and modelling techniques (MLOps).


As AWS Engineer, you:
 



  • Mentor and grow junior and mid-level members of the team;

  • Provide continuous technological intelligence to investigate and test new frameworks, tools or solutions;

  • Improve the pace of innovation and experimentation by building and improving ML infrastructure usage (AWS);

  • Ensure machine learning models meet various external and internal control requirements (model inventory, model explainability, model performance monitoring, and model lifecycle management);

  • Set up monitoring and alerting systems to detect and address performance bottlenecks and system anomalies promptly;

  • In short, apply MLOps principles along the end to end pipeline.