DepartmentAI & Data Engineering, AI & Data Engineering : Data Science
Company size5,001–10,000 people
PostedSep 16, 2026 · 3w ago
Verified live9h ago
At a glanceSummarised by Seekless from the posting.
Must have10
2 to 4 years of hands-on experience in advanced data science, machine learning, and AI engineering roles
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
Nice to have5
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
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
Skills
Python
SQL
TensorFlow
PyTorch
Sci-Kit Learn
CNTK
Keras
MXNet
KubeFlow
BentoML
Great Expectations
Evidently AI
Google Cloud Platform
PySpark
SAS
SPSS
R
R Studio
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:
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