Machine Learning Engineer
Indexed description
Primary Skills: Python, Machine Learning, TensorFlow, PyTorch, MLOps, AWS/Azure/GCP, Docker, Kubernetes
Required Skills
- Strong Python expertise with NumPy, Pandas, Scikit-learn.
- Hands-on experience with TensorFlow and/or PyTorch.
- Experience building and deploying ML solutions on AWS, Azure, or GCP.
- Knowledge of ML pipelines, model evaluation, CI/CD, version control, and MLOps practices.
- Strong understanding of ML system design, performance optimization, and monitoring.
- Experience with MLflow, SageMaker, Azure ML or similar MLOps platforms.
- Knowledge of Docker, Kubernetes, Spark, or Ray.
- Experience with LLMs, Deep Learning, Transformers, Vector Databases, and ML Governance.
- Prior experience leading ML projects or mentoring teams.
- Design, build, deploy, and maintain ML models and end-to-end ML pipelines.
- Perform data preparation, feature engineering, model training, evaluation, and optimization.
- Deploy and monitor models in production, including model drift detection and retraining.
- Collaborate with data engineering, DevOps, and business teams to deliver scalable ML solutions.
- Architect enterprise-scale ML systems and drive MLOps, automation, governance, and reliability initiatives.
- Mentor engineers, provide technical leadership, and evaluate emerging AI/ML technologies.
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