Senior Data Science Engineer

Job Description

Responsibilities

• Design, develop, and deploy production-grade machine learning and AI solutions.
• Fine-tune and optimize Large Language Models (LLMs) for domain-specific use cases involving technical and regulatory documentation.
• Build NLP pipelines capable of extracting structured rules and business logic from unstructured text sources.
• Design and implement Retrieval-Augmented Generation (RAG) architectures for intelligent querying of large technical knowledge bases.
• Develop time-series forecasting models for spend prediction, demand planning, and supply chain optimization.
• Build machine learning models for supplier risk scoring, anomaly detection, and operational analytics.
• Create scalable data extraction, transformation, and feature engineering pipelines from structured and unstructured data sources.
• Collaborate with Data Engineers and Backend Engineers to integrate AI models into enterprise applications through RESTful APIs.
• Optimize model performance, scalability, and reliability for large-scale processing workloads.
• Validate model outputs, analyze model performance, and continuously improve prediction quality and accuracy.
• Work closely with Product Managers, Domain Experts, and Engineering teams to ensure business requirements are translated into effective AI solutions.
  

Requirements

• 5+ years of commercial experience in Data Science, Machine Learning, or Artificial Intelligence.
• Strong programming skills in Python.
• Hands-on experience with Pandas, NumPy, Scikit-learn, TensorFlow, and/or PyTorch.
• Strong experience with Natural Language Processing and transformer-based architectures such as GPT, BERT, Llama, or similar models.
• Experience with prompt engineering, fine-tuning, and implementation of Large Language Models.
• Proven experience designing and implementing Retrieval-Augmented Generation (RAG) solutions.
• Experience building and deploying machine learning models into production environments.
• Strong understanding of supervised and unsupervised machine learning techniques.
• Experience developing forecasting, classification, and predictive analytics models.
• Solid knowledge of statistics, probability theory, experimentation, and model evaluation methodologies.
• Experience working with SQL and/or NoSQL databases.
• Familiarity with complex data formats such as JSON and XML.
• Understanding of REST APIs and backend integration using frameworks such as Flask or FastAPI.
• Ability to quickly understand complex domain-specific terminology and translate business requirements into scalable AI solutions.
• Experience working within Agile or Sprint-based delivery environments.
• Strong analytical thinking, problem-solving, and communication skills.

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