Roles and Responsibilities

• Own the architecture and end-to-end lifecycle of ML models, including data preparation, model training, evaluation, fine-tuning, and deployment.
• Design, train, fine-tune, and deploy state-of-the-art machine learning models, with a focus on NLP and deep learning.
• Apply hands-on experience with LLM techniques, including prompt engineering, model fine-tuning, LLMOps, function-calling, and retrieval-augmented generation (RAG).
• Utilize industry-standard ML frameworks, such as PyTorch, TensorFlow, Hugging Face Transformers, and PySpark, to build and optimize NLP models for various business use cases.
• Ensure efficient deployment of models into production environments, collaborating with DevOps and MLOps teams to streamline processes and implement CI/CD pipelines.
• Implement monitoring and evaluation strategies to continuously improve the performance, scalability, and maintainability of machine learning models in production.
• Automate key aspects of the model lifecycle, including data pipelines, model training, and retraining, to improve operational efficiency.
• Document the design, development, and deployment processes for machine learning models, ensuring clarity and reproducibility.
• Implement unit tests, conduct code reviews, and enforce software best practices to ensure the reliability and maintainability of the codebase.

Required Experience

• 8+ years of experience in software engineering, machine learning, data science, or artificial intelligence, with a strong focus on NLP and deep learning.
• Proven experience leading or mentoring teams in a software engineering or data science capacity.
• Strong proficiency in Python and PySpark.
• Hands-on experience with machine learning frameworks, including PyTorch, TensorFlow, and Hugging Face Transformers.
• Expertise in Natural Language Processing (NLP), machine learning, and deep learning.
• Familiarity with common ML tools and libraries such as NumPy, pandas, and scikit-learn.
• Experience working with Large Language Models (LLMs), including prompt engineering, fine-tuning, and LLMOps techniques.
• Exposure to Microsoft Azure or other cloud platforms for ML model deployment and operations.
• Experience with DevOps/MLOps/LLMOps practices for model deployment, monitoring, and automation.

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