(Senior) Machine Learning Engineer (MLOps)

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Data and Machine Learning (ML) are revolutionizing the way of doing business at a global scale. sennder is a European digital freight forwarder with a data-centric problem-solving approach to build the next generation of supply chain and road logistics services. Do you want to help us to shape the future?

We are looking for a (Senior) Machine Learning Engineer (MLOps) o join our central Machine Learning Engineering team as part of sennder’s Data department. The department’s mission is to “Relentlessly build exceptional value-adding products that inspire data-centricity in everything sennder does”. We’re a large, diverse team of ML/data engineers, data scientists, analysts and technical product people that are passionate by such mission. We want to attract and train world-class talent to form a incredible group that aim to provide you with the most productive and growth-friendly time of your career.

All Data dept. teams are multidisciplinar (e.g.: data engineering, MLOps, Data Science). The teams’ scope ranges from creating a unique technological backbone for our data platform to developing advanced predictive and prescriptive analytical services. Our proprietary data platform enables our operation teams to work in a distributed analytics-as-a-service ecosystem where everyone is empowered to be data driven in every decision they make. Our predictive and prescriptive analytics services bring us an unprecedented competitive advantage in terms of business automation across the industry. The obtained insights are then translated in a better customer experience, enabling a scalability flywheel data and revenues grow exponentially with each

other.

Every day, we acquire 3M+ new real-time data points (augmenting by the day!) about the cross-region road logistics industry in Europe. This data is used to build the future of logistics marketplaces where pricing optimization, load-to-carrier recommendation, load search and network optimization happen in an automated fashion.

Can you even imagine where we can go with your help? Let’s #keepOnTrucking... together!

YOUR MISSION:

• Design and maintain scalable ETL and DataOps / MLOps infrastructure usable in multiple scenarios and applications (e.g.: data science, advanced analytics);
• Define the new state-of-the-art for machine learning engineering in the road logistics services;
• Design and develop health and performance monitoring tools (MLOps) of data pipelines and the machine learning services in production;
• Design and improve heterogeneous, asynchronous and high-performance large-data processing pipelines from/to multiple sources/destinations;
• Operationalize innovative, data-intensive, end-to-end machine-learning(ML)-based decision engines;

YOUR PROFILE:

• Highly motivated with excellent communication and strong interpersonal skills;
• In-depth knowledge and professional working experience with software engineering, ETL pipelines and setting up machine learning workflows;
• Solid Python software engineering skills, including best practices like CI/CD and Git;
• Experience in designing/implementing virtualization services (e.g.: Docker/Kubernetes/Lambda Functions), microservices architectures (e.g.: RESTful API with FastAPI) and Kafka messaging queues in AWS cloud ecosystem;
• T-shaped mindset: actual expertise on a single area but the ownership/willingness to contribute to the product end to end in order to ensure a healthy value creation chain;
• Teamaholic. We don’t believe in super-heroes but rather in super-teams: teams that own products and are the single unit of work :)
• Experience with Agile philosophies (e.g.: Scrum, Scrumban, Kanban, XP) and project management tools (e.g.: JIRA);
• Fluent written and verbal communication in English; 

BONUS:

• 2+ years of experience in deploying and maintaining in production data pipelines working at scale which are fuelling and/or being fueled by machine learning models in production;
• Basic understanding of machine learning product lifecycle and the commonly associated components (MLOps): Experimental Environment (e.g.: Jupyter Notebook, MLflow) Workflow management (e.g.: Air-flow), Feature Stores (e.g.: Feast), DataOps/Pipelines (e.g.: Kafka), Model Deployment (e.g.: Terraform), Testing, Serving (e.g.: Docker, Flask). and Monitoring (e.g.: Datadog), Model Repository (e.g.: DVC) *

* List of technologies/methodologies is for illustrative purposes. You are not expected to have experience in each single one of these technologies/methodologies.