Role Overview
We are seeking a Generative AI Engineer to design, develop, and deploy advanced AI models within a large-scale cloud-based analytics environment supporting data-driven insights and innovation in the healthcare sector.
The role involves building generative AI solutions, enabling advanced analytics, and supporting the development of intelligent models that leverage large datasets to improve decision-making and operational outcomes.
Key Responsibilities
AI Model Development
- Design, develop, and optimize Generative AI and Large Language Models (LLMs).
- Implement prompt engineering, model fine-tuning, and evaluation techniques.
- Develop AI solutions to extract insights from structured and unstructured datasets.
Cloud & Platform Integration
- Deploy AI models within cloud-native environments.
- Integrate generative AI capabilities with data platforms and analytics tools.
- Optimize model performance, scalability, and reliability.
Data & Analytics
- Work with large-scale datasets to develop AI-driven insights and predictive models.
- Support the development of analytics models for research and decision-making.
- Collaborate with data teams to build reusable AI components.
Collaboration
- Work closely with data scientists, engineers, and analytics teams.
- Support cross-team collaboration on data and AI initiatives.
- Contribute to innovation in AI-driven analytics solutions.
Governance & Security
- Ensure compliance with data governance, security, and privacy standards.
- Implement responsible AI practices and model monitoring.
Required Skills
- Strong programming skills in Python.
- Experience with Generative AI, LLMs, or deep learning models.
- Hands-on experience with AI/ML frameworks (PyTorch, TensorFlow, Hugging Face, etc.).
- Experience working with cloud platforms (AWS, Azure, or GCP).
- Knowledge of ML pipelines, model deployment, and data engineering workflows.
Preferred Skills
- Experience with NLP and prompt engineering.
- Familiarity with MLOps and model lifecycle management.
- Experience working with large-scale analytics platforms or big data tools (Spark, Databricks, etc.).
- Exposure to AI solutions in regulated environments.
