ML, Engineer

About the Role

Master Works is seeking a highly skilled and motivated Machine Learning Engineer to design, develop, deploy, and scale cutting-edge machine learning models that drive data-driven products and intelligent systems. This pivotal role bridges the gap between Data Science, Software Engineering, and MLOps, demanding a robust blend of technical expertise and collaborative acumen. The ideal candidate will possess extensive hands-on experience in transforming theoretical models into robust, production-ready solutions that deliver measurable business impact.

The Machine Learning Engineer will collaborate closely with cross-functional teams, including Data Scientists, Product Managers, Software Engineers, and Data Engineers, to architect scalable AI solutions, optimize model performance, and advance enterprise-wide AI initiatives in alignment with industry best practices and governance standards.

Key Responsibilities

• Model Development & Deployment: Design, develop, train, optimize, and deploy machine learning models tailored to real-world business challenges. Ensure seamless integration into production environments with a focus on high availability, scalability, and performance.
• Solution Architecture: Translate complex business and product requirements into scalable ML and AI solutions, leveraging advanced feature engineering, model selection, hyperparameter tuning, and rigorous validation techniques.
• MLOps & Pipeline Management: Develop and maintain end-to-end machine learning pipelines, encompassing data ingestion, preprocessing, model training, validation, deployment, and continuous monitoring to ensure operational excellence.
• Performance Optimization: Monitor model performance, detect data drift and model decay, and initiate retraining or optimization cycles to sustain accuracy and reliability over time.
• Collaboration & Governance: Partner with cross-functional teams to align ML solutions with enterprise goals, participate in architecture discussions and design reviews, and uphold rigorous standards for reliability, scalability, governance, and security.
• Technical Excellence: Optimize models for latency, throughput, scalability, and cost efficiency while implementing experimentation frameworks such as A/B testing and offline evaluations to validate performance.
• Responsible AI: Champion Responsible AI principles, including fairness, explainability, governance, and model transparency, to ensure ethical and compliant AI deployments.

Requirements

• Education & Experience: Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field. Minimum of 3-7+ years of hands-on experience in Machine Learning, Applied AI, or equivalent technical domains.
• Technical Proficiency: Strong programming expertise in Python and/or Java/Scala, coupled with a deep understanding of machine learning algorithms, including supervised, unsupervised, and deep learning methodologies.
• Frameworks & Tools: Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn, along with deployment technologies like Docker, Kubernetes, or cloud-based ML services.
• Cloud & MLOps: Proficiency in cloud platforms including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), alongside MLOps tools such as MLflow, Kubeflow, Airflow, SageMaker, or Azure ML.
• Big Data & Advanced AI: Familiarity with big data technologies like Spark, Kafka, and Databricks, with a preferred background in NLP, Computer Vision, or Generative AI.
• Soft Skills & Collaboration: Demonstrated analytical thinking, problem-solving agility, and exceptional communication skills to thrive in Agile and cross-functional team environments.
• Enterprise AI: Experience building enterprise-scale AI or ML platforms and supporting production-grade AI systems with a focus on high-scale deployments and Responsible AI governance.

Location

This position is based at the client site, requiring on-site collaboration and engagement with the team.