Machine Learning Engineer

Company mission

We are a (still) small fine software engineering office near Mannheim. We are on a growth course from currently 8 employees to 1520 in the next 18 months.

Why

Our customers come from a wide variety of industries but one thing is always the same: there is a huge demand for our expert support in the areas of chat / voice bots libraries machine learning BI and cloud services etc. We always offer customized solutions.

Core Values

Fair Open Minded Reliable Social Innovative Healthy Happy Grateful

Requirements

*Ideally 2 to 3 years of professional experience in a consulting or IT environment.

*Indepth knowledge of Python.

*Willingness to learn and enjoy sharing knowledge.

*You convince us as a qualityconscious team player.

*You enjoy demonstrating your analytical problemsolving skills.

*You are confident in conducting technical dialogues in German (C1 minimum) and ideally in English.

Tasks & Responsibilities

*At engenious you will work as a Machine Learning Engineer (m/f/d) in a dedicated team of consultants and developers.

*Developing new and expanding existing machine learning and AI use cases for existing customers.

*You advise and support the implementation of data science and analytics projects

*You will manage your training experiments with modern MLOps frameworks such as MLFlow.

*You will familiarise yourself with different cloud platforms such as Microsoft Azure and IBM Watson.

*Our product is developed in an agile Scrum team.

*You will actively pass on your expert knowledge to our customers and help them to take over the operation of the created solution completely themselves.

Core Benefits

(1) Excellent worklife balance.

(2) 30 days of vacation (fulltime) parttime options transparent remuneration model.

(3) Continuous learning and personal development opportunities.

(4) Company health insurance pension plans and various employee benefits.

(5) Remote work possible

Required Experience

Minimum 2 years

Hiring Process

(1) Initial video call with HR.

(2) Onsite interview.

(3) Decision based on a practical task simulation & Offer