About the Role

Major accountabilities:

• Solution Design: Architect scalable GenAI solutions for drug discovery, medical writing automation, clinical trials, regulatory submissions, and real-world evidence generation.
• LLM Development & Optimization: Work with data scientists and ML engineers to develop, fine-tune, and optimize large language models (LLMs) for life sciences applications, such as scientific literature analysis, regulatory intelligence, and patient engagement.
• AI Infrastructure: Design GenAI solutions leveraging cloud platforms (AWS, Azure, GCP) or on-premises infrastructure while ensuring data security and regulatory compliance.
• MLOps & Deployment: Implement best practices for GenAI model deployment, monitoring, and lifecycle management within GxP-compliant environments.
• Compliance & Governance: Ensure GenAI solutions comply with regulatory standards (FDA, EMA, GDPR, HIPAA, GxP, 21 CFR Part 11) and adhere to responsible AI principles, including bias mitigation and exploitability.
• Performance Optimization: Drive efficiency in generative AI models, ensuring cost optimization and scalability while maintaining data integrity and compliance.
• Stakeholder Collaboration: Work with cross-functional teams, including platform teams, engineering teams from various supplier/vendors to align GenAI initiatives with enterprise and industry-specific requirements.
• Research & Innovation: Stay updated with the latest advancements in GenAI, multimodal AI, AI agents, and synthetic data generation to incorporate emerging technologies into the company s AI strategy.

Minimum Requirements

• Bachelor s or master s degree in computer science, AI, Data Science, Bioinformatics, or a related field.
• Experience: 12+ years experience in Big data, AI/ML development with at least 8 years in an AI Architect or GenAI Architect role in pharma, biotech, or life sciences.
• Technical Expertise:
• Strong proficiency in Generative AI, large language models (LLMs), multimodal AI, and deep learning for pharma applications.
• Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, Hugging Face, LangChain, Scikit-learn, etc.).
• Experience with data engineering, ETL pipelines, and big data technologies (Spark, Kafka, Databricks, etc.).
• Knowledge of cloud AI services (AWS Bedrock, Azure OpenAI, Google Vertex AI).
• Lead the deployment of a Lightweight LLM in a Pharma SaaS Platform
• MLOps & DevOps: Familiarity with CI/CD, containerization (Docker, Kubernetes), vector databases, and real-time model monitoring.
• Regulatory & Ethical AI: Understanding of AI governance, responsible AI principles, and compliance requirements for GenAI in pharma.
• Problem-Solving: Strong analytical and problem-solving skills with the ability to design innovative GenAI solutions for life sciences use cases.
• Communication & Leadership: Excellent communication skills to articulate GenAI strategies and solutions to technical and non-technical stakeholders