The business problem is to leverage advanced AI and machine learning techniques, including Generative AI and Vector Databases, to analyze large datasets, identify patterns, and provide actionable insights. This involves customizing and tuning models for large datasets, ensuring data versioning and traceability, and integrating LLMs into data pipelines for enhanced analytics and automation.
Requirements
- Python, ML, GenAI, VectorDB are pre-requisites
- Proficiency in machine learning and statistical modeling
- Technical skills in programming and data science tools : Proficiency in R, SQL, and Python, along with experience in data visualization, text analytics, and model deployment.
- Hands-on experience with LLMs such as GPT, BERT, or similar transformer-based models.
- Knowledge of natural language processing (NLP) techniques and how to fine-tune, deploy, and evaluate LLMs for various business applications.
- Familiarity wi th frameworks and tools used for LLM development and deployment.
- Ability to integrate LLMs into data pipelines and AI workflows to enhance analytics and automation capabilities.
Responsibilities
- Ability to work with large datasets,including creating clones of datasets for reformatting, normalization, and manipulation while preserving original data integrity.
- Experience running scalable machine learning algorithms, often using languages like Python, R, with capabilities to customize and tune models for large datasets,
- Understanding of dataset versioning and traceability techniques, important in regulated industries, often implemented through snapshot technologies.
- Proficiency in R, SQL, and Python, along with experience in data visualization, text analytics, and model deployment.
- Ability to analyze complex data, identify patterns, and provide actionable insights.
- Hands-on experience with LLMs such as GPT, BERT, or similar transformer-based models.
- Ability to integrate LLMs into data pipelines and AI workflows to enhance analytics and automation capabilities.
Other
- Analytical mindset and problem-solving skills
- Communication and collaboration skills : Ability to work with cross-functional teams including IT administrators, business analysts, and stakeholders.
- Security and compliance awareness: Understanding of data privacy, masking, and access controls to ensure secure handling of sensitive data
