ML Researcher/Data Engineer

Role Description

In this position, you will build customer prototypes as well as our composite AI platform. Most of the projects assigned to you are going to require an end-to-end approach; from customer communication, data understanding, and data preparation to building functioning and scalable code and AI/ML models and algorithms. This broad range of responsibilities falls into the rank of data science, ML, and research. You will problem-solve. Every new prototype we build is an unexplored territory and a hard nut to crack. Do whatever is necessary to find the best solution for the customer. Do not expect to have data of top-notch quality available, be ready to question everything and build your way toward the end goal. Our prototypes are being implemented in a broader span of industries; logistics (food delivery optimization), gaming (Gaming QA automation), and manufacturing (planning optimization for construction sites).

Typical tasks in the role

• Analyze both structured and unstructured data
• Create analytical solutions, from data gathering to display; construct data engineering pipelines
• Deploy, test, validate, and maintain AI / ML models and solutions according to business goals and customer needs
• Improve and broaden current ML frameworks and libraries

Upon productionalizing, after we as a company successfully deliver several prototypes and build our composite-AI platform, your tasks would gradually shift in DWH, data architecture, and MLOps areas to better streamline the increasing number and complexity of our solutions and products.

• Proficiency in Python (other languages are an advantage)
• Experience in MLOps (building and deploying machine learning models in production)
• Practical understanding of ML frameworks, libraries, and concepts
• Experience working with data ingestion tools
• Strong proficiency in data pipelines and workflow management tools (e.g., Airflow, Luigi, Kedro, Flyte, Dragster)
• Master's or Ph.D. degree in math, computer science, statistics, or a related field

Nice to have

• Working knowledge of Cloud-based solutions (e.g. AWS, Azure, GCP)
• Working knowledge of Google OR tools, IBM complex, Gurobi
• Experience with distributed computing frameworks, such as Spark, Ray, Celery
• Experience working with cloud Data Warehouse solutions (e.g., Snowflake, Redshift, BigQuery, Azure, etc.)