Key Responsibilities

GenAI Strategy Roadmap: Define and implement a generative AI architecture and roadmap aligned with business goals in pharma and life sciences.
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.
Cloud On-Prem AI Infrastructure: Design GenAI solutions leveraging cloud platforms (AWS, Azure, GCP) or on-premise 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 explainability.
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, and Drug Development teams, 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.

Required Qualifications

Bachelors or Masters degree in Computer Science, AI, Data Science, Bioinformatics, or a related field.
Experience: 8+ years in AI/ML development with at least 2 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.).
Proficiency in programming languages such as Python, R, or Java.
Knowledge of cloud AI services (AWS Bedrock, Azure OpenAI, Google Vertex AI).
Deploy 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.

Preferred Qualifications

Experience in GenAI applications for medical writing, automated clinical trial protocols, drug discovery, and regulatory intelligence .
Knowledge of AI explainability, retrieval-augmented generation (RAG), knowledge graphs, and synthetic data generation in life sciences.
AI/ML certifications from AWS, Google, or Microsoft.
Understanding of biomedical ontologies, semantic AI models, and federated learning .
Exposure to fine-tuning the LLM models will be a big plus
Exposure to SLM or domain LLM model will be a big plus
BioGPT PubMedBERT: NLP for biomedical literature and clinical trial analysis
Custom SLMs: Fine-tune Mistral 7B, Phi-2, or Gemma on pharma datasets

Dir. DDIT Dev. Data, Analyt, DS&AI (AI Architect)