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