Senior Veeva Vault QMS Expert: Support & Training

Python Technical Lead - Data Analysis, SQL / Machine Learning Engineer (Generative AI & Cloud)

Responsibilities
Generative AI Development
:
Design, develop, and fine-tune Generative AI solutions using models like Google's Gemini for tasks such as information extraction, document summarization, and report generation.
Architect and implement advanced Retrieval-Augmented Generation (RAG) systems to enhance model accuracy and provide verifiable, context-aware responses.
Research and apply emerging GenAI techniques, such as agentic frameworks, to build more autonomous and capable systems.
End-to-End Machine Learning
:
Design and deploy a wide range of ML models (classification, regression, forecasting, etc.) on Google Cloud Platform.
Build and maintain robust, automated MLOps pipelines for data preprocessing, feature engineering, model training, validation, and deployment using tools like Vertex AI, Big Query. etc.
Conduct deep data analysis to uncover insights, validate hypotheses, and guide feature engineering for improved model performance.
Collaboration & Strategy
:
Partner closely with data scientists, software engineers, and other business stakeholders to frame problem statements, define technical requirements and deliver integrated AI/ML solutions.
Champion best practices in software engineering and MLOps to ensure the quality, maintainability, and scalability of our machine learning systems.
Continuously evaluate and stay current with the latest advancements in the ML and GenAI landscape.

Qualifications

Experience:

5+ years of professional experience building and deploying machine learning models in a production environment.

Education:

Bachelor's degree in Computer Science, Data Science, Statistics, or a related quantitative field.
Programming:
Advanced proficiency in Python and its core data science/ML libraries (e.g., PyTorch, scikit-learn, Pandas).
Data & SQL:
Advanced proficiency in SQL for complex data manipulation, aggregation, and analysis.
Generative AI:
Demonstrable, hands-on experience in prompt engineering and/or fine-tuning Large Language Models (e.g., Gemini).
Cloud Platform:
Hands-on experience with a major cloud provider, with a strong preference for Google Cloud Platform (GCP).
MLOps:
Solid understanding of MLOps principles and experience with related tools (e.g., Vertex AI, CI/CD).

Qualifications
:
Master's or PhD in a relevant field.
Specific experience with GCP services like Vertex AI, Big Query, Google Cloud Storage, and GKE.
Experience building RAG systems from the ground up.
Proven ability to lead technical projects and mentor other engineers.