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

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