Contract Google Machine Learning Engineer Jobs in Exton, PA

Job Summary
We are seeking an experienced Machine Learning Engineer with strong hands-on expertise in building, training, deploying, monitoring, and maintaining production machine learning models. The role focuses on the end-to-end Machine Learning lifecycle, including data engineering, model development, offline evaluation, production deployment, monitoring, retraining, and A/B testing. The ideal candidate will have strong experience with Python, PySpark, large-scale data platforms, recommendation and ad personalization models, and production ML systems.
Key Responsibilities
• Design, build, train, validate, and deploy production Machine Learning models.
• Build recommendation and ad personalization models and support offline model evaluation and A/B testing in production.
• Perform feature engineering, feature selection, data preprocessing, and model optimization.
• Develop predictive models using Random Forest, XGBoost, CatBoost, Gradient Boosting, Ensemble Models, Regression, and Classification algorithms.
• Conduct hyperparameter tuning, cross-validation, and model evaluation using appropriate statistical and business metrics.
• Deploy production-ready inference pipelines and monitor models for drift, performance degradation, and retraining requirements.
• Build scalable PySpark pipelines for ingesting, cleaning, transforming, and preparing large enterprise datasets.
• Develop efficient ETL/ELT pipelines supporting production ML workflows.
• Optimize Spark jobs for performance and scalability.
• Work with Databricks, Snowflake, Delta Lake, or similar big data platforms.
• Write clean, maintainable, production-quality Python code.
• Build scalable REST APIs and backend services supporting ML inference.
• Participate in code reviews and follow software engineering best practices.
• Build automated testing, deployment, monitoring, and retraining pipelines.
• Deploy ML models into production environments and implement monitoring and alerting strategies.
• Track model performance using appropriate business and technical metrics.
• Collaborate with Data Engineering and Software Engineering teams to operationalize ML solutions.
• Troubleshoot production issues and optimize model and system performance.
• Mentor junior Machine Learning Engineers and provide technical guidance on model development and production best practices.
Required Qualifications
• 5+ years of hands-on Machine Learning Engineering experience.
• Strong expertise in Python programming.
• Strong experience writing production PySpark code.
• Strong understanding of data science, statistics, and Machine Learning fundamentals.
• Strong understanding of deep learning and NLP fundamentals.
• Experience building and deploying production Machine Learning models.
• Experience with Databricks, Snowflake, or similar large-scale data platforms.
• Strong understanding of the complete ML lifecycle, including data preparation, feature engineering, model training, hyperparameter tuning, model evaluation, production deployment, monitoring, and retraining.
• Experience developing scalable data pipelines and distributed data processing solutions.
• Strong SQL skills.
• Experience with Scikit-learn and Machine Learning algorithms including Random Forest, XGBoost, CatBoost, Gradient Boosting, Regression, and Classification.
• Experience with model evaluation, feature engineering, hyperparameter tuning, cross-validation, model monitoring, and drift detection.
• Experience with ETL/ELT and distributed data processing.
• Experience working in Agile software development environments.
• Experience writing production-quality Python code and building scalable ML solutions.
• Experience deploying and monitoring Machine Learning models in production environments.
Preferred Qualifications
• Experience building transformer-based recommendation models.
• Familiarity with multi-armed bandit approaches.
• Experience with Retrieval-Augmented Generation (RAG) solutions.
• Experience with LangChain or LangGraph.
• Experience integrating LLM APIs into enterprise applications.
• Experience with Vector Databases.
• Experience with MLOps tools such as MLflow.
• Experience with cloud platforms including AWS or Azure.
• Experience with Docker.
• Experience with CI/CD pipelines and Git.
• Experience with Generative AI, RAG, or AI agents.