Senior Scientist - Generative Ai For Workflow, Applications And Plug-Ins

Experteer Overview In this role you will lead the evolution of reusable GenAI-enabled content-authoring workflows across Ru**amp; D, enabling scalable document generation and regulatory readiness.You will shape enterprise-grade capabilities on an internal Agentic Platform, aligning with cross-functional teams and regulatory needs.You’ll drive integration of AI into scientific workflows, delivering measurable impact on efficiency and quality.You will partner with data, quality, and platform teams to build a reusable, compliant authoring ecosystem.Compensaciones / Beneficios - Lead technical strategy and implementation of GenAI-enabled document-authoring workflows - Translate workflows into reusable authoring capabilities and differentiate enterprise-common vs document-specific logic - Develop roadmaps across multiple document classes and RuD functions - Establish patterns for evidence retrieval, grounding, drafting, citation, structured content, review and revision - Partner with Data teams to define source-data requirements and connectivity - Collaborate with Evaluation u** Quality to ensure content-quality, faithfulness, traceability, and regulatory-readiness - Integrate reusable QC/content-review into authoring workflows - Assess external vendor capabilities as modular services within architecture - Coordinate with Foundation Platform Team on reusable plugin and platform requirements - Provide technical leadership to engineers, contractors, and data science partners - Drive prototype workflows toward scalable enterprise capabilityResponsabilidades - Master’s degree in AI/ML, Computer Science, Engineering, Bioinformatics, or related field with 2+ years of hands-on GenAI experience - Hands-on experience with multi-agent frameworks such as LangGraph, DSPy, memO, or similar - Strong Python and modern deep-learning frameworks (PyTorch, TensorFlow) - Experience building retrieval-augmented generation (RAG) systems (chunking, embeddings, vector databases, reranking) - Knowledge of uncertainty quantification, probabilistic modelling, or information theory - Experience implementing structured generation, schema enforcement, and model/tool orchestration for complex workflows - Positive, motivated, collaborative approach in multidisciplinary environmentsRequisitos principales - annual bonus - vacation days - parential leave - bereavement leave - caregiver leave - volunteer leavee’,’well-being reimbursement’,’insurance plans

leadership to engineers, contractors, and data science partners - Drive prototype workflows toward scalable enterprise capabilityResponsabilidades - Master’s degree in AI/ML, Computer Science, Engineering, Bioinformatics, or related field with 2+ years of hands-on GenAI experience - Hands-on experience with multi-agent frameworks such as LangGraph, DSPy, memO, or similar - Strong Python and modern deep-learning frameworks (PyTorch, TensorFlow) - Experience building retrieval-augmented generation (RAG) systems (chunking, embeddings, vector databases, reranking) - Knowledge of uncertainty quantification, probabilistic modelling, or information theory - Experience implementing structured generation, schema enforcement, and model/tool orchestration for complex workflows - Positive, motivated, collaborative approach in multidisciplinary environmentsRequisitos principales - annual bonus - vacation days - parential leave - bereavement leave - caregiver leave - volunteer