Lead Machine Learning Engineer (NLP/Python)

Job Description

We have a contract need for a Machine Learning Engineer with a strong background in Machine Learning (ML) and Natural Language Processing (NLP). The ideal candidate will have extensive experience in developing and deploying ML models, particularly in the domain of NLP.

Responsibilities

• Develop and implement ML algorithms and models, with a focus on NLP tasks
• Design and optimize data pipelines for efficient data processing using PySpark and SQL.
• Conduct exploratory data analysis to gain insights and identify patterns in large datasets.
• Evaluate and select appropriate ML frameworks and libraries to support project goals.
• Stay updated with the latest advancements in ML and NLP technologies, and integrate them into existing workflows when applicable.
• Ensure the scalability, reliability, and performance of ML models in production environments.

Skills

Strong proficiency in Python programming and popular ML/NLP libraries such as TensorFlow, PyTorch, NLTK, spaCy, etc. Solid understanding of data processing frameworks like PySpark and SQL, with hands-on experience in building and optimizing data pipelines. Excellent problem-solving skills and the ability to work independently as well as in a team environment.

Qualifying Questions

• How many yrs of professional exp do you have?
• What is your educational background?
• What certifications do you hold?
• What technical skills are you most proficient in?
• How many yrs of exp with Machine Learning (ML) Engineer?
• How many yrs of exp with Natural Language Processing (NLP)?
• Using a scale of 1 to 10, rate your NLP skills
• How many yrs of Python programming?
• Using a scale of 1 to 10, rate your OO Python programming skills
• How Many Yrs Of Exp With The Following
  • Develop and implement ML algorithms and models
  • Natural Language Toolkit (NLTK)
  • Artificial Intelligence (AI)
  • TensorFLow
  • PyTorch
  • spaCy
  • Deep Learning
  • Data analysis to gain insights and identify patterns in large datasets.
  • Developing and deploying ML models, particularly in the domain of NLP.
  • Data Science
  • Design and optimize data pipelines for efficient data processing
  • Pandas
  • Django
  • Numpy
  • PySpark
  • SQL