Machine Learning Engineer (Senior Level)
Remote
JD:
Primary Skills: Python, Machine Learning, TensorFlow, PyTorch, MLOps, AWS/Azure/GCP, Docker, Kubernetes
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.
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.
Preferred Skills
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.
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.
Key Responsibilities
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.
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.
Years of Experience: 12.00 Years of Experience
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