GenAI & MLOps Lead, AI Platform Architect

Rentokil Initial Careers; Crawley; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

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

Job summary

Rentokil Initial is expanding its Group AI Team to design, build and deploy production-grade AI solutions across global operations. You will lead engineering efforts for Agentic AI, mentor engineers, and partner with data platforms to scale multi-agent workflows and guardrails.

You will bridge business requirements with technical execution, shaping MVPs into reliable production applications for enterprise use across regions. A strong background in LLMs, MLOps, and cloud platforms is essential.

Qualifications

• Expert-level experience building complex LLM-powered systems and multi-agent workflows.
• Deep practical understanding of ML algorithms, NLP, and enterprise deployment of LLMs.
• Strong data engineering knowledge with ELT/ETL and cloud platforms.

Responsibilities

• Design Agentic Systems and multi-agent workflows for scalable AI solutions.
• Define engineering standards and CI/CD pipelines for AI assets.
• Lead production deployments and guardrails for enterprise AI safety and compliance.
• Mentor engineers and translate business use cases into technical sprints.
• Collaborate with data platform and enterprise architecture for data assets.

Skills

LLM Orchestration

AI Architecture

Team Leadership

Python

LangGraph / LangChain

Education

Bachelor's or Master's in CS/SE/AI

Tools

Vertex AI

Docker

Kubernetes

BigQuery

GCP

Job description

Rentokil Initial is expanding its Group AI Team to design, build and deploy production-grade AI solutions across global operations. You will lead engineering efforts for Agentic AI, mentor engineers, and partner with data platforms to scale multi-agent workflows and guardrails.

You will bridge business requirements with technical execution, shaping MVPs into reliable production applications for enterprise use across regions. A strong background in LLMs, MLOps, and cloud platforms is essential.