Overview

Senior Lead AIML Engineer R

Responsibilities

• Design develop and maintain scalable machine learning pipelines using Java Python or Scala to support advanced data science solutions.
• Implement optimize and deploy distributed computing solutions leveraging frameworks such as Spark DASK or Ray to handle large-scale data processing and model training.
• Lead the end-to-end delivery of low-latency inferencing solutions ensuring robust performance and reliability in production environments.
• Build and enhance deep learning pipelines for forecasting and personalization using frameworks such as PyTorch and TensorFlow.
• Collaborate with cross-functional agile teams to design and implement RESTful web services and integrate machine learning components into broader service-oriented architectures.
• Utilize real-time streaming technologies like Kafka or Storm to enable real-time data ingestion and processing for machine learning applications.
• Drive experimentation model evaluation and statistical analysis using advanced techniques such as hypothesis testing regression and probabilistic graphical models.
• Ensure quality and reproducibility of machine learning workflows by leveraging tools such as Git JIRA and containerization platforms like Docker or PCF.

Qualifications

• Senior Lead AI/ML Engineer
• Primary Skills
• Hypothesis Testing T-Test Z-Test Regression (Linear Logistic) Python/PySpark SAS/SPSS Statistical analysis and computing Probabilistic Graph Models Great Expectation Evidently AI Forecasting (Exponential Smoothing ARIMA ARIMAX) Tools(Kube Flow BentoML) Classification (Decision Trees SVM) ML Frameworks (TensorFlow PyTorch Sci-Kit Learn CNTK Keras MXNet) Distance (Hamming Distance Euclidean Distance Manhattan Distance) R/ R Studio
• Advanced proficiency in Java Python or Scala for software and ML pipeline development
• Hands-on experience with distributed computing frameworks such as Apache Spark DASK or Ray
• Deep understanding of machine learning frameworks including TensorFlow PyTorch Sci-Kit Learn Keras and MXNet
• Expertise in statistical analysis and computing including hypothesis testing T-Test Z-Test and regression methods
• Experience with real-time data streaming tools such as Kafka or Storm
• Knowledge of building and deploying deep learning models for forecasting and personalization
• Proficiency in RESTful web services and service-oriented architecture design
• Experience with containerization technologies such as Docker or PCF
• Familiarity with ML workflow orchestration tools like Kube Flow or BentoML
• Proficiency with Git for version control

Nice to have

• Experience with Great Expectations or Evidently AI for model monitoring and validation
• Hands-on expertise in SAS SPSS or R/R Studio for advanced statistical modeling
• Knowledge of advanced forecasting techniques including Exponential Smoothing ARIMA and ARIMAX
• Familiarity with classification algorithms such as Decision Trees and SVM
• Understanding of distance metrics like Hamming Euclidean and Manhattan Distance
• Experience in container orchestration and deployment within cloud or hybrid environments
• Experience using agile project management tools such as JIRA
• Bachelors degree in Computer Science Information Systems Data Science or a closely related field
• Relevant certification in machine learning data engineering or distributed computing (such as TensorFlow Developer Certificate or Databricks Certified Associate Developer for Apache Spark)