Collaborate with data scientists to develop, train, and evaluate machine learning models.
Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring.
Leverage cloud platforms (AWS, GCP, Azure) for ML model development, training, and deployment.
Implement DevOps/MLOps best practices to automate ML workflows and improve efficiency.
Develop and implement monitoring systems to track model performance and identify issues.
Conduct A/B testing and experimentation to optimize model performance.
Work closely with data scientists, engineers, and product teams to deliver ML solutions.
Stay updated with the latest trends and advancements

Solid foundation in machine learning algorithms and techniques
Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow)
Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD)
Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)
Doctorate degree OR Master’s degree and 2 years of Computer Science experience OR Bachelor’s degree and 4 years of Computer Science experience OR Associate’s degree and 8 years of Computer Science experience OR High school diploma / GED and 10 years of Computer Science experience
Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.
Excellent analytical and troubleshooting skills.
Strong verbal and written communication skills
Ability to work effectively with global, virtual teams
High degree of initiative and self-motivation.
Ability to manage multiple priorities successfully.
Team-oriented, with a focus on achieving team goals.
Ability to learn quickly, be organized and detail oriented.
Strong presentation and public speaking skills.

A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
group medical, dental and vision coverage
life and disability insurance
flexible spending accounts
A discretionary annual bonus program
stock-based long-term incentives
Award-winning time-off plans
Flexible work models, including remote and hybrid work arrangements, where possible

Senior Machine Learning Engineer

Key skills