Senior Specialist, Data Science & Artificial Intelligence II

Key Accountabilities

• Develop test and validate machine learning, deep learning, and Generative AI models including LLM-based applications.
• Implement prompt engineering strategies and retrieval-augmented generation (RAG) pipelines.
• Perform feature engineering, model tuning, and performance optimization.
• Prepare, cleanse, and transform structured and unstructured datasets.
• Support deployment of ML and GenAI models using MLOps and LLMOps practices.
• Conduct structured evaluation of LLM outputs including hallucination detection and quality scoring.
• Integrate AI solutions via APIs into enterprise systems.
• Document model design assumptions, risks, and validation results.
• Ensure compliance with data governance, cybersecurity, and ethical AI guidelines.
• Support monitoring, retraining, and lifecycle management of deployed models.

• Python, SQL, ML frameworks:
  Tensor Flow, PyTorch, scikit-learn.
• Prompt engineering, RAG pipelines, vector databases.
• Model fine-tuning and evaluation, data preprocessing, feature engineering.
• Basic cloud deployment concepts.
• MLOps and LLMOps fundamentals.
• Analytical problem-solving, results orientation, collaboration, continuous improvement, accountability.

Bachelor's degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative field.

4-6 years in data science, machine learning, or applied AI roles.