Machine Learning Engineer

MLOPS Engineer

Ready to break boundaries in the analytical world? Accelerate your ML engineering career with our advanced training program. We fully pay this program as well as your salary. Your job? Working as a ML(ops) engineer at one of our clients like Lely, ING, ASML or KLM on challenging projects. What kind of projects?

✅ Help data-driven teams to build production-ready, scalable machine learning systems

✅ Productionizing machine learning models

✅ Maintain and improve automated ML pipelines and ML experimentation infrastructure focusing on scalability, usability, and performance

The role itself

• 2 fulltime weeks of hands-on bootcamp training where you’ll focus on writing deployable code, containerization, ML(ops) engineering and cloud fundamentals

• Work 4 days a week as a ML(ops) Engineer at one of our clients like Heineken, Rabobank, ASML, Lely, FedEx, Vattenfall

• Join us every Friday at the Xccelerated office in Amsterdam/ on remote. Here you will get ongoing training and project support from our technical leads

• At the end of your first year, you get the opportunity to join the partner organization directly

As a ML(ops) Engineer you will work together with other medior- and senior team members on challenging projects for our clients. In these projects you will work on data science & ML related topics, such as predictive software solutions, fraud detection or data engineering challenges such as building data pipelines, developing on cloud architectures and productionizing machine learning models.

Besides this, you like big data tools and programming frameworks to ensure that the raw data gathered from data pipelines are used to build production-ready scalable applications driven by data and AI.

About Xccelerated

Xccelerated is an initiative within the Xebia Group, accelerating growth of Data, Cloud & AI competence. Our 13-month advanced training programs integrate hands-on learning and skill development with working at one of our partner companies. It is an intense learning journey for who have proven their potential and are ready for the next step in their career.

Are you one of them? Join our dream team of 45 great people. Each member of our team brings a unique specialty and passion to our shared mission: eliminate the shortage of highly skilled Data & AI professionals. To achieve our goal, we empower each other to reach their full potential and inspire others to do the same. Or as Nikki, our ML engineer says: “The skills I learned at Xccelerated are incredibly valuable – I use them every day at work. After the bootcamp, I had the feeling that I wanted to rewrite all my previous projects”.

What Xccelerated offers

💰 Good salary

🛫 25 vacation days

📙 Kick off with 2 full-time weeks of hands-on bootcamp training

📊 Technical trainings & Innovation days for a full year (every week)

✅ Challenging assignments

💻 Macbook and Iphone

🍎 Lunches, amazing coffee and snack bar

🎒Flexibility in working from home & at the office

This is you

From a personal perspective, you are customer focused and have a strong can-do mentality. You focus on results, have a desire to complete challenging tasks and learn while doing. Next to this, you have:

✅ A technical bachelor’s or master’s degree (e.g. Data Science, Econometrics, Artificial Intelligence)

✅ 2-4 years of work experience as a ML Engineer or Data scientist

✅ Statistical knowledge but are mainly experienced with end to end systems and able to run, deploy and monitor them

✅ Knowledge of open-source technologies like Spark, Kafka, Airflow or Kubernetes

Interested? We’d love to meet you!

Please get in touch! Contact Samantha Alves Goncalves at sgoncalves@xccelerated.io or by phone at 0613889770.

Application Process

• First interview with one of our recruitment consultants 

• Making a technical assessment (from home) you will present to one of or Tech Leads (home/on remote)

**We aim to complete an application process (from application to offer) within 1-3 weeks. As you will understand, this also depends on your own availability.