ML/AI Engineer - Classical ML

In this role, you will help transform machine learning solutions into reliable, scalable, production-ready systems. You will collaborate with Data Science teams to operationalize machine learning models, build robust data processing pipelines, and improve the efficiency of AI-driven applications. Your work will contribute to the development of production recommendation systems and the implementation of modern MLOps and LLMOps practices. You will leverage cloud technologies, big data platforms, and machine learning frameworks to deliver innovative technical solutions. Working alongside experienced engineers and technical experts, you will help establish best practices across the machine learning lifecycle. This fully remote opportunity offers the autonomy, flexibility, and continuous learning needed to make a meaningful impact in a collaborative, international environment.

• At least 5 years of data engineering experience, including the last 3 years focused on building and maintaining data processing solutions.

• Hands-on experience developing and deploying production-grade machine learning recommendation systems.

• At least 5 years of experience writing production-ready Python code, including microservices, APIs, or similar applications.

• At least 3 years of experience developing production-ready code for machine learning applications.

• Practical experience with MLOps and LLMOps tools and platforms, such as Azure Machine Learning, Azure AI, or Google Cloud Vertex AI.

• Hands-on experience with Databricks and familiarity with big data processing technologies, including Spark, PySpark, and Hive, in environments such as Databricks, Amazon EMR, or equivalent platforms.

• Strong understanding of machine learning and AI concepts, including algorithm types, ML frameworks, model performance and efficiency metrics, model lifecycle management, and AI architectures.

• Good understanding of cloud computing concepts and architectures, with practical knowledge of cloud services, preferably on Microsoft Azure or Google Cloud Platform.

• Experience in at least one of the following areas: data warehousing, data lakes, data integration, data governance, machine learning, deep learning, or MLOps.

• Proven experience designing, implementing, and maintaining data pipelines and scalable data processing solutions.

• Strong problem-solving and critical-thinking skills, with the ability to analyze complex technical challenges and develop effective solutions.

• Excellent communication and collaboration skills, with the ability to work effectively in a team, support colleagues, and take ownership of assigned tasks and deliverables.

• Fluency in written and spoken English.

• A proactive mindset, willingness to learn, and motivation to contribute to a collaborative, knowledge-sharing environment.