Machine Learning Specialist

10 hours ago


Chennai, Tamil Nadu, India Golden Hues Consultant Full time

Job Overview

We are looking for a highly skilled Machine Learning Specialist to join our team in Chennai. As a key member of our data science team, you will be responsible for developing and deploying machine learning models, analyzing large datasets, and providing insights to inform business decisions.

The estimated salary for this role is $150,000 - $200,000 per annum, depending on experience. You will have the opportunity to work with a variety of technologies, including TensorFlow, PyTorch, and scikit-learn, and collaborate with a talented team of engineers and data scientists.

Key Responsibilities:

  • Develop and deploy machine learning models: Design, build, train, and evaluate various models, including deep learning, natural language processing, and time series analysis models.
  • Data processing and feature engineering: Clean, transform, and prepare large datasets for model training and evaluation.
  • LLM and RAG development: Build and fine-tune LLMs for specific tasks related to voice and data analytics.
  • Prompt-to-SQL generation: Develop models that can translate natural language queries into SQL queries.
  • Data insights and visualization: Develop algorithms and tools to extract meaningful insights from data and present them clearly and concisely.
  • Classifier development: Build and train classifiers for various tasks, such as sentiment analysis, topic classification, and intent recognition.
  • Model optimization and deployment: Optimize model performance for speed and accuracy. Deploy models to production environments using appropriate tools and technologies.

Requirements:

  • 2-4 years of experience developing and deploying machine learning models in a production environment.
  • Strong understanding of fundamental machine learning concepts, including model selection, evaluation metrics, bias-variance tradeoffs, and regularization techniques.
  • Solid grasp of neural networks, including different architectures (e.g., CNNs, RNNs, Transformers), activation functions, back propagation, and optimization algorithms.
  • Deep understanding of Transformer models, including attention mechanisms, self-attention, and different Transformer variants (e.g., BERT, GPT).
  • Experience with classification modeling techniques, including handling imbalanced datasets, multi-class classification, and model interpretation.
  • Strong programming skills in Python and experience with relevant libraries such as TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers.


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