Our project is a groundbreaking initiative focused on Artificial Intelligence (AI), aimed at enabling high-value AI use cases through cutting-edge platforms. The goal is to establish a Center of Excellence in AI and GenAI, an innovative hub where top AI professionals collaborate, share best practices, and explore new ideas. This center will play a crucial role in advancing Ten-Year Ambitions, fostering innovation and excellence in AI across the organization.
This role presents an exciting opportunity to contribute to the pharmaceutical sector’s future through AI-driven solutions. Leveraging the latest advancements, this project’s strategy includes enhancing conversational AI applications, boosting developer productivity, and leveraging enterprise search technologies with trusted partners. By using low-code/no-code frameworks and established AI workbenches (Dataiku, AWS Sagemaker, Kamino), this project seeks to push the limits of AI in delivering solutions across complex use cases.
responsibilities :
Design, implement, and fine-tune GenAI models and machine learning pipelines.
Deploy and manage models in production environments using best practices in MLOps.
Continuously monitor and optimize model performance, ensuring scalability and efficiency.
Assess large language models within specific domains and adapt as necessary.
Work alongside data scientists, software engineers, and business stakeholders to identify requirements and deliver effective solutions.
Communicate complex technical concepts clearly to non-technical audiences.
Integrate GenAI models seamlessly with existing systems and workflows.
Support a range of consulting projects, from brief proof-of-concepts to comprehensive production solutions.
Develop scalable MLOps infrastructure, including CI/CD pipelines, version control, and automated testing processes.
Oversee cloud-based resources and infrastructure for efficient model training and deployment.
Stay informed on the latest GenAI and MLOps advancements.
Implement best practices for model testing, deployment, and monitoring.
Conduct post-deployment evaluations and propose enhancements.
Ensure models and data pipelines adhere to regulatory standards and data security guidelines.
Address potential biases and maintain ethical AI practices in accordance with industry standards.
requirements-expected :
Strong programming proficiency, particularly in Python (R is also a plus).
Experience with MLOps, CI/CD, version control, and automated testing.
Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps tools (SageMaker, Vertex AI, Azure ML Studio).
Hands-on experience with Docker, Kubernetes, and containerization.
Knowledge of Vector Databases and machine learning integration frameworks like Langchain or Llamaindex.
Solid background in data engineering, infrastructure automation (e.g., Terraform, AWS CloudFormation), and cloud-native Kubernetes services.
Over 2 years in Git, Linux fundamentals, and Bash scripting.
Designing and implementing CI/CD pipelines (e.g., GitLab, Argo CD).
Working in complex consulting environments, especially within enterprise-level AI or MLOps projects.
A strong understanding of data science principles, such as train/test data management, overfitting, and classification.
Knowledge of DevOps and Agile methodologies.
Strong problem-solving abilities, excellent attention to detail, and superior troubleshooting skills.
Effective communication skills and the ability to work well both independently and in team environments.
English proficiency at B2 level or higher.
Hands-on experience with GenAI technologies and applications.
Exposure to MLOps tools (e.g., MLflow, Kubeflow) and familiarity with cloud infrastructure and services across major platforms.
An interest or experience in fields such as computer vision, NLP, or predictive modeling.
