Job
Description
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
