Machine Learning Expert

👋🏼 We're Nagarro.

We are a digital product engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (17 500+ experts across 37 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in!

By this point in your career, it is not just about the tech you know or how well you can code. It is about what more you want to do with that knowledge. Can you help your teammates proceed in the right direction? Can you tackle the challenges our clients face while always looking to take our solutions one step further to succeed at an even higher level? Yes? You may be ready to join us.

Looking for Data/Machine Learning Engineer to build robust, cloud-native and hybrid data/machine learning pipelines. You'll work closely with cross-functional teams, including Data Scientists and Solution Architects, to support model research and early-stage development.

Responsibilities:

• Build cloud-native and hybrid data/machine learning pipelines.
• Collaborate with Solution Architects, Cloud/Backend Engineers, Data Security Engineers, Data Scientists, and Algorithm Developers.
• Develop enterprise-grade tools to support long-term ML research.
• Provide technical support for early-stage research and development.
• Organize, clean, and manage raw datasets.
• Prepare data for predictive and prescriptive modeling.
• Identify and implement opportunities to automate data processes (e.g., curation, validation).

• Degree in Computer Science or a related field; Master’s degree is a plus.
• 10+ years of practical experience working with Python and Machine Learning/ data engineering technologies.
• Strong grasp of the Python ecosystem (e.g., NumPy, Pandas, Scikit-learn).
• Familiarity with coding best practices (e.g., TDD, ATDD).
• Understanding of application architecture and design patterns.
• Testing strategies and version control.
• Extensive experience with storage technologies (relational databases, NoSQL, object storage).
• Solid understanding of Machine Learning workflows and the infrastructure needed to support them.
• Experience with HPC and AWS-based data engineering and analytics platforms.
• Hands-on with ETL and data integration tools (e.g., Talend, Kubeflow, Spark, Tableau, Flume, Kafka, Databricks).
• Comfortable working in a Linux environment.

• Experience working in global or international teams is a plus.
• Data engineering certifications (e.g., AWS Solution Architect, IBM Certified Data Engineer) are a plus