Machine Learning Engineer

Who we are:

Open-minded intellectuals who embrace emerging technologies on our mission to create innovative Software Engineering Solutions that will impact millions of users around the world.

We are looking for

We are seeking an experienced and results-driven Machine Learning Engineer to design, develop, and deploy scalable machine learning solutions that drive impactful business outcomes. In this role, you will build end-to-end ML pipelines, optimize large-scale data processing workflows, and collaborate closely with cross-functional teams to deliver production-ready AI solutions that support innovation and operational excellence.

What you will do:

• Designing, building, and deploying machine learning models using Python and leading frameworks such as TensorFlow and PyTorch, ensuring scalability, reliability, and high performance.
• Developing and maintaining end-to-end machine learning pipelines, from data ingestion and feature engineering to model deployment and monitoring.
• Leveraging Amazon SageMaker’s built-in algorithms and tools to streamline model development, training, and deployment workflows.
• Utilizing Apache Spark on AWS EMR to process and analyze large-scale datasets, enabling efficient feature extraction and data preparation for machine learning applications.
• Working with AWS data services such as Amazon S3 and Redshift for data storage, processing, and analytics.
• Managing the machine learning lifecycle using MLflow, including experiment tracking, model versioning, governance, and deployment management.
• Utilizing Apache Airflow to orchestrate, automate, and monitor data processing pipelines and machine learning workflows.
• Collaborating closely with data engineers to ensure seamless integration of machine learning models into production environments.
• Monitoring model performance, identifying optimization opportunities, and implementing improvements to ensure long-term business value.
• Contributing to the adoption of best practices in software engineering, including testing, version control, CI/CD, and code quality standards.

What you need to have:

• 4+ years of experience in Machine Learning Engineering, including hands-on experience building and deploying machine learning models at scale.
• Strong programming skills in Python and practical experience with Apache Spark for distributed data processing.
• Hands-on experience with AWS services such as SageMaker, EMR, Redshift, and S3 for developing end-to-end machine learning solutions.
• Experience building production-grade machine learning systems and writing clean, maintainable, and efficient code.
• Familiarity with software engineering best practices, including version control, automated testing, and CI/CD pipelines.
• Experience with MLflow or similar tools for experiment tracking, model management, and lifecycle governance.
• Familiarity with Apache Airflow or equivalent workflow orchestration tools.
• Strong analytical and problem-solving skills.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.
• Good English knowledge (upper intermediate level).

What's in it for you:

• Extended compensation and benefits package
• Continuous learning opportunities to enhance your professional and soft skills
• A great working environment with people who put their heart, mind, and soul into everything they do and understand the importance of team spirit

We really welcome open-minded and committed people:

• Eager to take on new challenges and learn new things;
• Who put their heart, mind, and soul into everything they do;
• Who enjoy sharing knowledge and understand the importance of team spirit.