Machine Learning Engineer

About the role

Master Works is looking for a highly skilled Machine Learning Engineer to design, build, deploy, and scale ML models that power data‑driven products and intelligent systems. The role sits at the intersection of data science, software engineering, and MLOps, requiring hands‑on experience turning prototypes into production‑ready solutions.

Key responsibilities

• Design, develop, train, optimize, and deploy machine learning models for real‑world business use cases.
• Build and maintain end‑to‑end ML pipelines covering training, validation, deployment, and monitoring.
• Collaborate with data scientists, product managers, software engineers, and data engineers to deliver scalable AI solutions.
• Ensure models meet reliability, scalability, governance, and security standards.
• Monitor model performance, data drift, and decay, and orchestrate retraining cycles.
• Participate in architecture, design, and code reviews following engineering best practices.

Required profile

• Bachelor’s degree in Computer Science, AI, Data Science, Software Engineering or a related field.
• 3–7+ years of hands‑on experience in machine learning, applied AI, or related technical roles.
• Strong programming experience in Python and/or Java or Scala.
• Solid understanding of supervised, unsupervised and deep learning algorithms.
• Experience deploying models with Docker, Kubernetes and cloud‑based ML services (AWS, Azure, GCP).
• Familiarity with MLOps tools such as MLflow, Kubeflow, Airflow, SageMaker or Azure ML.

Required skills

• Python
• Java
• Scala
• TensorFlow
• PyTorch
• Scikit‑learn
• Docker
• Kubernetes
• AWS
• Microsoft Azure
• Google Cloud Platform
• MLflow
• Kubeflow
• Airflow
• SageMaker
• Azure ML
• Apache Spark
• Kafka
• Databricks