Lead Data Scientist - AI

As part of our Group AI Team, we are scaling our internal capabilities to deliver cutting-edge, production-grade AI solutions across our global operations.

We are establishing an “Agentic Factory” - a dedicated, high-velocity delivery team focused on designing, building, and deploying genAI & ML solutions, multi-agent workflows, and automation systems to solve complex business problems.

The primary goals of the team include:

• Leading the technical design and architecture of AI solutions, in addition to building and running an AI platform to deliver business-specific Use Case solutions
• Collaborating with the Data Platform to team in ingesting and transforming data from multiple systems, modeling data, and engineering data marts to create reusable data assets, including developing and implementing machine learning models, genAI, and Agentic AI
• Creating an operating a company wide agentic AI solution platform to help scale up AI capabilities across all functions and regions
• Building AI models and a data science platform that enables Rentokil to derive significant value from AI, from machine learning to gen AI and beyond, and ensuring the quality and reliability of AI solutions deployed on the platform
• Support, govern and enable company-wide adoption of emerging AI technologies, We are seeking a pragmatic, highly technical individual to lead the engineering efforts within our Agentic Factory.

Sitting directly alongside our AI Delivery Manager and AI Product Owner, you will bridge the gap between business requirements and technical execution. Together with the AI & Data Architect, you will provide architectural oversight, defining engineering best practices, and mentoring a talented team of AI engineers & Data Scientists, while remaining hands-on enough to solve complex engineering bottlenecks; you will support Use Case Design and feasibility assessments, ensuring opportunities explored are achievable and scalable.

You will support the Head of Engineering as the execution arm for AI responsibilities, extending your focus beyond the AI team to support and enable other IT teams across the organisation., Technical Leadership & Engineering

• Design Agentic Systems: Design and scale robust, secure, and production-ready multi-agent workflows, orchestrations, and advanced RAG architectures, in collaboration with Enterprise Architecture principles. Drive delivery by designing and building agentic solutions, spanning from piloting to full implementation
• Define Engineering Excellence: Establish strict coding standards, code review processes, testing frameworks, and evaluation metrics for generative AI applications. Support and strictly enforce the standards set by the Head of Engineering and Technical Architect
• Cloud & Platform Integration: Partner closely with our GCP and Data Engineering teams to build seamless LLMOps/MLOps CI/CD pipelines, ensuring scalable and cost-effective model deployment via Vertex AI and containerized environments. Take ownership of building and maintaining robust LLMOps pipelines
• AI Safety & Guardrails: Implement robust evaluation frameworks, latency monitoring, and automated guardrails to ensure enterprise-grade safety, security, and compliance
• AI Safety & Guardrails: Implement robust evaluation frameworks, latency monitoring, and automated guardrails to ensure enterprise-grade safety, security, and compliance
• Internal AI Enablement & Prompt Lifecycle: Manage the engineering workflows, CI/CD pipelines, version control, and evaluation frameworks for internal developer-facing AI assets, including prompt libraries and automated testing agents

Team Mentorship & Delivery

• Grow the team: Act as a technical mentor to a team of intermediate and junior AI Engineers, fostering a culture of continuous learning, clean code, and agility
• Pragmatic Delivery: Collaborate with the AI Product Owner and Business Analysts to translate abstract business use cases into structured, achievable technical sprints
• Drive MVP to Production: Shift the team’s focus from sandboxed proof-of-concepts (PoCs) to reliable, resilient applications deployed to production for global users, leading AI engineering for AI team solutions and actively supporting junior engineers through this transition

Evolve AI Maturity

• Support the company’s evolving AI strategy, providing an expert voice on Use Case identification, platform identification and tool selection
• Advise the AI portfolio Lead in scaling impact and AI capability across the company, beyond the Group AI Team
• Stay up to date on market trends, new opportunities, and the changing landscape of AI technologies

Expert-level experience building complex LLM-powered systems and multi-agent workflows using frameworks like LangGraph, LangChain, AutoGen, or ADK.

Artificial Intelligence & Machine Learning

Deep practical understanding of machine learning algorithms, natural language processing (NLP) techniques, and the optimization of large language models for enterprise deployment.

Data Engineering & Cloud Computing

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).

MLOps/LLMOps Pipelines

Proven track record of architecting pipelines for model deployment, performance tracking, hyperparameter tuning, and containerized workflows using Docker and Kubernetes (GKE/Vertex AI Pipelines).

Team Leadership & Mentorship

Extensive experience leading technical delivery, defining engineering milestones, running code reviews, and successfully mentoring junior or intermediate engineering talent., Works with high autonomy under broad strategic guidance. Accountable for defining and meeting technical, architectural, and delivery goals, establishing milestones, and assigning tasks., * Python: Advanced, production-grade proficiency (Pandas, NumPy, FastAPI/Flask) with an absolute focus on writing clean, modular, and highly testable code

• SQL: Expert querying, database design, partitioning, and optimization strategies for large-scale BigQuery environments, * Google Cloud Certified Cloud Engineer
• Google Cloud Certified Professional Data Engineer / Cloud AI Engineer

Degree Qualification or Equivalent

A Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a highly quantitative field is desirable; however, a proven track record of architecting and shipping production-grade commercial AI systems is highly valued and may substitute for specific academic credentials. IT HNC/HND courses will also be accepted.

• Competitive salary and bonus scheme
• Hybrid working
• Rentokil Initial Reward Scheme
• 23 days holiday, plus 8 bank holidays
• Employee Assistance Programme
• Death in service benefit
• Healthcare
• Free parking