Machine Learning Engineer

Strong proficiency in ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, XGBoost, and deep learning architectures.
Experience with cloud platforms (AWS, GCP, Azure) and deploying ML models using SageMaker, Vertex AI, or Azure ML.
Familiarity with MLOps practices, including model versioning, CI/CD pipelines for ML (e.g., MLflow, Kubeflow, Airflow), and monitoring deployed models.
Expertise in data preprocessing and feature engineering, using tools such as Pandas, NumPy, and Apache Spark.
Strong programming skills in Python (preferred) or Scala, with experience in containerization tools like Docker and Kubernetes.
Understanding of distributed computing and parallel processing frameworks such as Ray or Dask for scalable ML workloads.
Experience in deploying ML models to production, optimizing inference latency, and ensuring model robustness in real-world applications.

Leads and supports multiple Machine Learning projects, ensuring alignment with business objectives and data needs.
Oversees the Kanban process for managing ML model development, deployment, and maintenance.
Engages in technical discussions both within the company and with external data partners, fostering collaboration and knowledge sharing.
Drives the design and architecture of machine learning solutions by deeply understanding data usage and identifying opportunities to integrate new data sources.
Implements emerging technologies and methodologies in machine learning, ensuring state-of-the-art solutions and continuous innovation.
Mentors and guides junior ML engineers, promoting best practices, scalability, and efficiency in model development.
Collaborates closely with data engineers and scientists to enhance ML workflows, ensuring models are reliable and production-ready.

Experience in data pipelines and analytics for video-game development
Experience in Advertising industry
Experience in online businesses where transactions happen without human intervention.