Title: Machine Learning Engineer Location: Bellevue, WA; Thousand Oaks, CA; San Diego, CA; South San Francisco, CA; Chicago, IL; Evanston, IL; Boston, MA; New York, NY; Princeton, NJ; Philadelphia, PA Duration: FTE The Challenge: As a Machine Learning Engineer, you will be a key contributor to the client's AI Practice, building cutting-edge AI-powered data products and solutions. Your work will involve developing and deploying machine learning models, optimizing their performance, and ensuring their reliability in production environments. Roles & Responsibilities: • Design, develop, and deploy machine learning pipelines, including feature engineering, model training, and inference. • Scale machine learning algorithms to handle massive datasets and meet stringent SLAs. • Build and enhance ML engineering platforms and components. • Implement robust ML Ops practices, including model monitoring, evaluation, and retraining. • Write clean, efficient, and well-tested code. • Collaborate effectively with cross-functional teams, including data scientists, engineers, and product managers. • Stay up-to-date with the latest advancements in machine learning and AI. Qualifications: • Bachelor's or master’s degree in computer science, Statistics, or a related field. • 2-4 years of experience in machine learning model development and deployment. • Strong programming proficiency in Python and PySpark. • Experience with ML platforms (e.g., Dataiku, SageMaker, MLflow). • Expertise in cloud platforms (AWS, Azure, GCP) for model deployment. • Solid understanding of machine learning algorithms and techniques. • Proficiency in data structures, algorithms, and software design patterns. • Excellent problem-solving and analytical skills. • Strong communication and collaboration abilities. Preferred Qualifications: • Experience with Spark or other distributed computing frameworks. • Knowledge of DevOps, CI/CD, and data security. • Experience with data engineering in big data systems.