Senior AI/ML Engineer - R01571293

Senior AI/ML Engineer

Job requirements
Experience Range: With at least 2 to 4 years of hands-on experience in advanced data science, machine learning, and AI engineering roles Key Responsibilities: Design and develop advanced machine learning models using classic algorithms and deep learning techniques to address complex business challenges and deliver measurable improvements Conduct comprehensive exploratory data analysis (EDA) and statistical analysis, including hypothesis testing, regression, and classification, to extract actionable insights from large datasets Implement, optimize, and deploy AI/ML models on Google Cloud Platform (GCP), ensuring scalability, reliability, and efficient integration into production environments Collaborate with cross-functional teams to define data requirements, validate model outputs, and integrate AI solutions seamlessly into existing workflows Utilize KubeFlow and BentoML for efficient model orchestration, deployment, and monitoring, ensuring robust operational performance Perform rigorous forecasting using methods such as exponential smoothing, ARIMA, and ARIMAX to support data-driven business planning Apply probabilistic graph models and advanced statistical methods to enhance predictive accuracy and interpretability of AI solutions Maintain high standards for data quality and model performance using frameworks like Great Expectations and Evidently AI, tracking key metrics and outcomes Required Skills: Proficiency in Python and SQL for data manipulation, analysis, and model development Hands-on experience with classic machine learning algorithms and deep learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet Expertise in statistical analysis including hypothesis testing, T-Test, Z-Test, and regression (linear, logistic) Experience with classification techniques such as decision trees and support vector machines (SVM) Knowledge of forecasting methods including exponential smoothing, ARIMA, and ARIMAX Ability to implement and interpret probabilistic graph models Familiarity with tools for model deployment and orchestration such as KubeFlow and BentoML Competence in computing and analyzing distance metrics (Hamming, Euclidean, Manhattan) Experience with data quality frameworks such as Great Expectations and Evidently AI Preferred Skills: Experience with GenAI and Agentic AI technologies Hands-on expertise with PySpark, SAS, or SPSS for large-scale statistical computing Proficiency in R and R Studio for statistical modeling and data visualization Desired Qualifications: Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline Certification in machine learning or data science from recognized platforms such as TensorFlow Developer Certificate or Google Professional Machine Learning Engineer Certification in cloud technologies, for example Google Cloud Certified - Professional Data Engineer