Senior Specialist, Data Science & Artificial Intelligence

This position involves designing, developing, and deploying machine learning and Generative AI solutions to address specific business challenges. The focus is on ensuring technical robustness, model performance, and the responsible implementation of AI technologies.

Key responsibilities include the development, testing, and validation of machine learning, deep learning, and Generative AI models, particularly those utilizing large language models (LLMs). The role requires the implementation of prompt engineering and retrieval-augmented generation (RAG) pipelines, alongside feature engineering, model tuning, and performance optimization.

You will be responsible for preparing and transforming both structured and unstructured datasets, supporting the deployment of ML and GenAI models through MLOps and LLMOps practices. Additionally, conducting structured evaluations of LLM outputs—including hallucination detection and quality scoring—is essential.

Integration of AI solutions into enterprise systems via APIs and thorough documentation of model designs, assumptions, risks, and validation results is also a key aspect of the role. Compliance with data governance, cybersecurity, and ethical AI guidelines must be ensured, as well as the monitoring, retraining, and lifecycle management of deployed models.

Minimum Qualification, Experience and Competencies

• Minimum Qualification: Bachelor’s degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative field.
• Minimum Experience: 4–6 years in data science, machine learning, or applied AI roles.
• Skills Required:
  • Proficiency in Python, SQL, and ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
  • Knowledge of prompt engineering and RAG pipelines.
  • Experience with model fine-tuning and evaluation.
  • Skills in data preprocessing and feature engineering.
  • Understanding of basic cloud deployment concepts and MLOps/LLMOps fundamentals.
  • Strong analytical problem-solving capabilities.
  • Results-oriented approach with emphasis on collaboration, continuous improvement, and accountability.