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Machine Learning Engineer/Architect - Python/PySpark|Multiple|6+Years

  • HuQuo
  • Multiple
  • 6 - 13 Yrs

Job Closed

Job Description

  • Proven experience as a Machine Learning Engineer, Architect, or similar role.
  • Understanding of data structures, data modeling, and software architecture.
  • Deep knowledge of mathematics, probability, statistics, and algorithms.
  • Ability to write robust code in Python, PySpark, and Scala.
  • Experience working in a big data environment (Hadoop/AWS).
  • Familiarity with machine learning frameworks (e.g., Keras, PyTorch) and libraries (e.g., scikit-learn).
  • Excellent communication and stakeholder management skills, with a strong team orientation.
  • Outstanding analytical and problem-solving abilities.
  • Bachelor's degree in Computer Science, Mathematics, or similar field; Master's degree is a plus.
  • Good to have: 4+ years of experience with a bank

Job Responsibilities

  • Design and deploy machine learning systems.
  • Research and implement suitable ML algorithms and tools.
  • Develop machine learning applications based on requirements.
  • Choose appropriate datasets and data representation methods.
  • Conduct statistical analysis and fine-tuning based on test results.
  • Train and retrain systems as needed.
  • Extend existing ML libraries and frameworks.

Location

Gurgaon, Haryana, India

Pune, Maharashtra, India

About Company

HuQuo (derived from Human Quotient) is the next stage in the evolution of HR. In an industry thirsty for innovation, HuQuo is a fresh perspective towards people and organizations. At HuQuo,we do not look at human beings as mere resources, but as an ocean of untapped potential. Human Quotient goes beyond IQ and EQ. It’s the holistic representation of all human potential. We strategically manage this Human quotient. We believe that every organization has a human like individuality which can help nurture and flourish those individuals that resonate with it. Our endeavor is to bring such high resonance individuals and organizations together. We will achieve this through extensive use of analytics and deep immersion to understand the core of the organizations and people we work with.