Senior AI/ML Engineer - R01570503

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