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Role Overview
Machine Learning Engineer at Masterworks, based at Client Site. The role sits at the intersection of Data Science, Software Engineering, and MLOps, requiring strong hands-on experience in transforming models into production-ready solutions.
Role Purpose
Design, build, deploy, and scale machine learning models that power data-driven products and intelligent systems. Work closely with Data Scientists, Product Managers, Software Engineers, and Data Engineering teams to develop scalable AI solutions, optimize model performance, and support enterprise AI initiatives aligned with engineering best practices.
Key Responsibilities
Model Design and Development
- Design, develop, train, optimize, and deploy machine learning models for real-world business use cases.
- Implement feature engineering, model selection, tuning, validation, and evaluation techniques.
- Solid understanding of machine learning algorithms, including supervised learning, unsupervised learning, and deep learning techniques.
Production Deployment and Operations
- Develop and deploy ML models into production environments with high availability, scalability, and performance.
- Build and maintain machine learning pipelines including training, validation, deployment, and monitoring workflows.
- Monitor model performance, data drift, and model decay, and support retraining and optimization activities.
- Optimize models for latency, throughput, scalability, and operational cost efficiency.
Governance and Quality
- Ensure ML solutions meet reliability, scalability, governance, and security standards.
- Apply Responsible AI principles including fairness, explainability, governance, and model transparency where applicable.
- Participate in architecture discussions, design reviews, and code reviews following engineering best practices.
Stakeholder Collaboration
- Translate business and product requirements into scalable ML and AI solutions.
- Collaborate with Data Scientists, Product Managers, Software Engineers, and Data Engineers across cross-functional teams.
- Support the development and maintenance of high-quality and reliable data pipelines.
Experimentation and Evaluation
- Implement experimentation and evaluation frameworks including A/B testing and offline evaluations.
Qualifications & Experience
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
- Minimum 3–7+ years of hands-on experience in Machine Learning, Applied AI, or related technical roles.
- Experience building enterprise-scale AI or ML platforms.
- Experience supporting production-grade AI systems and high-scale ML deployments.
Skills & Competencies
Programming and Languages
- Strong programming experience in Python and/or Java, or Scala.
ML Frameworks and Tools
- Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Experience with MLOps tools and platforms such as MLflow, Kubeflow, Airflow, SageMaker, or Azure ML.
Deployment and Infrastructure
- Experience deploying ML models using Docker, Kubernetes, or cloud-based ML services.
- Hands-on experience with cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud (GCP).
Big Data Technologies
- Familiarity with big data technologies, including Spark, Kafka, and Databricks.
Engineering Fundamentals
- Strong knowledge of software engineering principles, data structures, and algorithms.
Domain Expertise (Preferred)
- Background in NLP, Computer Vision, or Generative AI.
- Familiarity with Responsible AI and AI governance practices.
Soft Skills
- Strong analytical thinking, problem-solving, collaboration, and communication skills.
- Experience working within Agile and cross-functional delivery teams.
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