This role focuses on building and deploying advanced AI/ML models to solve complex business problems while ensuring seamless integration into production environments using GCP and Kubernetes. The candidate will lead cross-functional collaboration to validate insights and optimize scalable AI solutions for measurable impact.
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
