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AI Summary
Lead the design and evolution of AI and ML architecture for a production-grade compliance platform in the pharmaceutical industry. Focus on optimizing LLM-driven retrieval pipelines and implementing regulatory-aligned explainability mechanisms.
This is a remote position.
We are looking for a Data Science Lead to join a high-impact AI programme operating within a strictly regulated pharmaceutical environment. The role focuses on leading the machine learning and GenAI architecture behind a production-grade compliance platform that automates regulatory validation of marketing materials. This is a strategic, enterprise-scale initiative requiring strong ownership, architectural thinking, and hands-on depth in LLM-driven systems.
Responsibilities
- Design and evolve AI and ML architecture supporting compliance validation workflows
- Design and optimise LLM-driven retrieval and validation pipelines (RAG-based systems)
- Own evaluation frameworks, benchmarking strategies, and continuous improvement loops
- Implement explainability and traceability mechanisms aligned with regulatory standards
- Collaborate closely with ML Engineers and Backend teams on productionisation of AI components
- Drive decisions around embeddings, vector databases, and retrieval strategies
- Ensure reproducible, testable, and high-quality AI workflows
- Support scaling the platform into an enterprise-grade AI solution
- Lead technical discussions across product, engineering, and compliance stakeholders
Requirements
- Strong hands-on background in Data Science and applied Machine Learning
- Proven experience designing and deploying LLM-based systems in production
- Practical experience with Retrieval Augmented Generation architectures
- Experience with vector databases and embedding pipelines
- Strong Python expertise and familiarity with modern AI frameworks
- Experience designing model evaluation and validation frameworks
- Ability to operate in regulated or compliance-heavy environments
- Strong ownership mindset and ability to influence architectural decisions
- Confident communication skills in cross-functional environments
Nice to have
- Experience in pharmaceutical, healthcare, or other regulated industries
- Exposure to explainable AI methodologies or frameworks
- Experience with document intelligence and NLP-heavy pipelines
- Background in enterprise-scale AI platforms rather than proof-of-concept environments
Benefits
- Solid, competitive salary
- Work in a multinational environment on international projects
- Comprehensive healthcare
- Long-term B2B contract with a stable project pipeline
- Fully remote working model
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