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Lead an embedded AI squad from discovery to production: harden and deliver AI prototypes, design reusable agentic AI patterns, ensure security and data governance, integrate AI with enterprise systems, codify reference implementations, mentor engineers, and measure outcomes to scale solutions.

Design, build, evaluate, and scale production-ready AI solutions (LLMs, NLP, retrieval, and generative AI) to support scientific discovery. Develop RAG systems, semantic search, summarization, QA, ranking, recommendation, and evaluation frameworks. Integrate knowledge graphs and domain metadata, partner with engineering and product teams, lead technical decisions, and mentor data scientists.

The Principal Architect will define enterprise architecture, drive architecture strategy, govern adherence to principles, collaborate with teams, and communicate complex technical concepts.

Design, develop, and deploy ML, NLP, and generative-AI solutions (including LLMs and RAG) to improve search, retrieval, recommendation, and question-answering across scientific publications and datasets. Build scalable pipelines, evaluation frameworks, and production-quality Python code while collaborating with engineering, product, UX, and domain experts to measure and improve model reliability, relevance, and user impact.

Lead a data science team to deliver data-driven solutions in life sciences, applying machine learning, NLP, and AI methods. Oversee project management, stakeholder collaboration, and ensure high-quality standards in data science practices.

The Software Engineering Lead will manage a team, drive technical strategy for CK AI Service, and enhance delivery efficiency while mentoring engineers.

Lead the design and delivery of scalable search and retrieval systems, guide a team of engineers, and own technical outcomes for a shared search platform. Build services and data workflows (Elasticsearch, Spark, Airflow), write production Python/Java code, integrate vector/semantic search and LLM/GenAI features, mentor engineers, and ensure observability, performance, and cross-team alignment.

As a Senior Incident Response Engineer, improve security technology, lead incident response investigations, analyze security events, and develop remediation programs.

Lead end-to-end change management for complex, technology-enabled programmes. Shape change strategies, assess impacts, engage stakeholders and sponsors, design communications and enablement, measure readiness and adoption, advise smaller initiatives, and help develop change standards and AI-enabled approaches to improve delivery.

Build, test, and ship LLM-powered, agentic AI solutions (RAG, tool-augmented agents) into enterprise workflows. Implement retrieval pipelines, observability, guardrails, CI/CD, and instrumentation; collaborate with stakeholders and deliver reusable patterns and handovers.