Machine Learning Engineer

vor 2 Monaten


Heidelberg, Deutschland Recruitment Room Vollzeit
Company mission

We are a (still) small, fine software engineering office near Mannheim. We are on a growth course from currently 8 employees to 15-20 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.

In-depth knowledge of Python.
Willingness to learn and enjoy sharing knowledge.
You convince us as a quality-conscious team player.
You enjoy demonstrating your analytical problem-solving 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 work-life balance.

(2) 30 days of vacation (full-time), part-time 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) On-site interview.

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



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