Apply📍 United States
🧭 Full-Time
💸 130000.0 - 200000.0 USD per year
🔍 Software Development
- 5+ years of experience developing, deploying, and maintaining ML models in production environments.
- Proficiency in Python and common ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch, XGBoost).
- Strong foundation in statistics, linear algebra, probability, and optimization.
- Deep understanding of a range of ML techniques (regression, classification, clustering, NLP, deep learning).
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Familiarity with containerization and orchestration tools (Docker, Kubernetes).
- Solid understanding of software engineering principles, version control (Git), and CI/CD workflows.
- Design, train, and evaluate machine learning models using best-in-class frameworks.
- Architect scalable ML solutions and pipelines, from feature engineering to deployment.
- Implement rigorous testing, validation, and monitoring processes to ensure model reliability in production.
- Work closely with data engineers to shape the data architecture required for robust ML workflows.
- Build efficient ETL pipelines to clean, preprocess, and transform large-scale datasets.
- Partner with product managers, engineers, and business stakeholders to define ML use cases.
- Collaborate with software engineers to integrate ML models into production-grade APIs and applications.
- Translate complex ML concepts into business-relevant insights and recommendations.
- Stay current with advancements in machine learning, AI, and related fields.
- Experiment with new algorithms, architectures, and tools to continuously enhance our capabilities.
- Contribute to a culture of experimentation, technical excellence, and intellectual curiosity.
AWSDockerPythonETLGCPGitKubernetesMachine LearningMLFlowPyTorchAzureData engineeringTensorflowCI/CD
Posted about 13 hours ago
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