Dir. DDIT Dev. Data, Analyt, DS&AI

Job Description

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 withinGxP-compliantenvironments.
- Compliance Governance:Ensure GenAI solutions comply withregulatory 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 inGenAI, multimodal AI, AI agents, and synthetic data generationto 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 inGenerative AI, large language models (LLMs), multimodal AI, and deep learningfor 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 inGenAI applications for medical writing, automated clinical trial protocols, drug discovery, and regulatory intelligence.
- Knowledge ofAI explainability, retrieval-augmented generation (RAG), knowledge graphs, and synthetic data generationin life sciences.
- AI/ML certifications from AWS, Google, or Microsoft.
- Understanding ofbiomedical 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-tuneMistral 7B, Phi-2, or Gemmaon pharma datasets

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