Design, develop, and implement predictive models, machine learning algorithms, and decision engines to address complex business challenges
Conduct experiments, tune hyperparameters, and apply advanced modeling techniques to improve the accuracy, scalability, and efficiency of new and existing models
Partner with cross-functional stakeholders to translate business requirements into technical specifications for ML solutions
Execute full-stack engineering tasks including data preprocessing, feature engineering, model deployment, API development, and integration with enterprise systems
Engineer scalable, secure, compliant, and production-grade ML pipelines with modern cloud-native technologies
Design and implement distributed training workflows, online inference systems, and low-latency serving architectures
Propose new platform solutions and enhancements that improve capabilities and performance of existing scheduling systems and decision engines
Partner with system architects to ensure adequate hardware, compute resources, and platform configurations (Kubernetes, Cloud Run) to support machine learning applications
Define key metrics, monitoring frameworks, and automated alerts to continuously evaluate the performance and drift of deployed models
Create clear, comprehensive documentation and support guides for newly implemented tools
Provide technical guidance and mentorship to junior engineers and data scientists

5+ years of experience in machine learning engineering, data science, or related technical role
Strong proficiency with Python and AI/ML frameworks (LLMs, TensorFlow, PyTorch, Scikit-learn, etc.)
Strong hands-on expertise with Google Cloud Platform (GCP), training, pipelines, deployment, BigQuery, and Airflow
Solid understanding of software engineering principles, and API development
Proven track record of building and deploying ML models into production systems at scale
Bachelor's degree in Computer Science, Data Science, Engineering, or related technical field

Senior Machine Learning Engineer

Key skills