Machine Learning Engineer Role
Core
Build reproducible, scalable, and supportable machine learning and AI model lifecycle platforms, enabling safe transition from experimentation to production.
Role type
Senior Machine Learning Engineer (MLOps & Platform)
Builds
Reproducible training/validation pipelines, model-serving systems, feature stores, model registries, and automated release controls.
Domain
Artificial Intelligence / Machine Learning Operations (MLOps)
Deliverable
production ML models
Required skills
Software engineering, model development, training and inference pipelines, MLOps, containerization, cloud/on-prem compute, artifact management, automated deployment, monitoring and drift analysis, feature engineering, version control, testing, production debugging.
Preferred skills
Experience with predictive models, computer vision, NLP, ranking, anomaly detection, recommender systems, edge inference, generative AI, distributed processing, infrastructure as code.
Technologies
Python, SQL, Java, model frameworks, distributed-processing tools, cloud ML services, container orchestration, GPUs, feature stores, model registries, experiment tracking, infrastructure as code.
Responsibilities
Automate training and validation, manage features and model artifacts, optimize inference, implement MLOps, monitor data quality and model performance, create shared tooling for data scientists and application engineers.
Seniority
Senior, hands-on IC
