Principal Data Scientist - AI, MLOps & GenAI Lead
HybridCrawley, England, United Kingdom
Job Summary
Design and scale robust, secure, production-ready multi-agent workflows, orchestrations, and advanced RAG architectures while building an AI platform to deliver business-specific use case solutions. Establish strict coding standards, code review processes, and evaluation metrics for generative AI applications, partnering with GCP and Data Engineering teams to build seamless LLMOps/MLOps CI/CD pipelines via Vertex AI. Implement automated guardrails for safety, security, and compliance, and manage engineering workflows for internal developer-facing AI assets including prompt libraries and automated testing agents. Mentor a team of intermediate and junior AI engineers, translating abstract business use cases into structured technical sprints to shift focus from proof-of-concepts to reliable, resilient applications deployed to global users. Provide architectural oversight and technical mentorship to drive engineering excellence across the Group AI Team and support broader IT enablement.
Required Qualifications
- Expert-level experience building complex LLM-powered systems and multi-agent workflows using frameworks like LangGraph, LangChain, AutoGen, or ADK
- Deep practical understanding of machine learning algorithms, natural language processing (NLP) techniques, and the optimization of large language models for enterprise deployment
- Strong proficiency in designing optimized ELT/ETL pipelines and managing data lake/data warehouse architectures
- Hands-on experience with Google Cloud Platform (GCP) services including Vertex AI, BigQuery, Cloud SQL, and Google Cloud Storage (GCS)
- Proven track record of architecting pipelines for model deployment, performance tracking, hyperparameter tuning, and containerized workflows using Docker and Kubernetes (GKE/Vertex AI Pipelines)
- Extensive experience leading technical delivery, defining engineering milestones, running code reviews, and successfully mentoring junior or intermediate engineering talent
- Advanced, production-grade proficiency in Python (Pandas, NumPy, FastAPI/Flask) with an absolute focus on writing clean, modular, and highly testable code
- Expert querying, database design, partitioning, and optimization strategies for large-scale BigQuery environments
- Works with high autonomy under broad strategic guidance
- Exerts major technical influence across the organization, partners, and peers
- Manages diverse, highly complex, and unpredictable technical challenges
- Advises executives on AI trends, risks, and tools
- Bridges the gap between technical developers and business teams, showcasing strong leadership, creativity, and ethical problem-solving
Desired Qualifications
- Google Cloud Certified Cloud Engineer
- Google Cloud Certified Professional Data Engineer / Cloud AI Engineer
- A Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a highly quantitative field
- A proven track record of architecting and shipping production-grade commercial AI systems
- IT HNC/HND courses
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