Machine Learning Engineer

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Experience:

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About the Role

Machine Learning Engineer

Python, DevOps tools, and excellent communication skills

Key Responsibilities

• Design, develop, and deploy

  machine learning models

  for real-world applications.
• Build and optimize

  end-to-end ML pipelines

  using

  PySpark

  and

  MLflow

  .
• Work with structured and unstructured data using

  Pandas

  ,

  NumPy

  , and related libraries.
• Train and fine-tune models using

  scikit-learn

  ,

  TensorFlow

  , or

  PyTorch

  .
• Experiment and integrate with

  LLMs

  such as

  OpenAI GPT

  and

  Hugging Face Transformers

  .
• Collaborate with

  data engineers, product managers, and software developers

  for seamless deployment.
• Monitor and enhance

  model performance, scalability, and reliability

  .
• Ensure

  versioning, reproducibility, and lifecycle management

  of ML experiments using

  MLflow

  .

Required Skills

• Strong proficiency in

  Python programming

  .
• In-depth understanding of

  machine learning algorithms

  , model training, and evaluation.
• Hands-on experience with

  PySpark

  for large-scale data processing.
• Proficient in

  MLflow

  ,

  Pandas

  ,

  NumPy

  , and

  Scikit-learn

  .
• Familiarity or hands-on work with

  LLMs (OpenAI, Hugging Face Transformers)

  .
• Knowledge of

  MLOps principles

  and deployment best practices.
• Strong communication and collaboration abilities.

Preferred Qualifications

• Bachelor’s or Master’s degree

  in Computer Science, AI/ML, Data Science, or a related field.
• Experience with

  cloud ML platforms

  such as

  AWS SageMaker

  ,

  Azure ML

  , or

  GCP Vertex AI

  .
• Strong

  analytical thinking and problem-solving

  skills.