AI Ops Engineer

AI Ops Engineer

Working Arrangement: Remote (Flexible)
Outside IR35
FTC OR Freelance/Contract

My client is a high-growth, product-led SaaS business operating at scale across the UK, supporting high-volume, business-critical workflows. The business is investing in becoming more data-driven and operationally efficient, with AI as a key lever for improving productivity, reducing cost, and enhancing customer experience. This is a fixed-term project role focused on embedding AI into core operations.

The Role
I am looking for a commercially minded AI Operations consultant to identify inefficiencies, design and implement AI-driven solutions, and embed AI as a core operational capability. You will work cross-functionally across Customer Operations, Marketing, Training, Finance and Strategy to automate workflows, reduce manual overhead, improve reporting, increase support efficiency, and leverage internal data platforms to drive intelligent automation, with a focus on delivering measurable improvements in cost, efficiency, and operational performance.

Core Responsibilities
● Define and execute an AI operations roadmap, prioritising initiatives based on ROI and business impact
● Identify inefficiencies and design AI-driven workflow automation across core systems (e.g. CRM workflows, support ticket triage, reporting automation)
● Build and deploy automation pipelines using tools such as Zapier/Make, LLM APIs, and API/webhook integrations
● Leverage internal data platforms to create event-driven workflows, predictive insights, and AI-enabled reporting
● Establish governance frameworks, ensure GDPR compliance, and develop internal AI playbooks
● Drive adoption and training across teams to embed AI into day-to-day operations

Required Experience
● Proven experience delivering AI-driven workflow automation in a SaaS or product-led environment
● Strong experience across core SaaS operational systems, ideally covering CRM, support, billing and product analytics (Salesforce, Zendesk, Zuora, Pendo)
● Strong API integration capability and experience with automation tooling
● Proficiency in Python and SQL
● Experience working with LLM APIs and prompt engineering

Great to have
● Additional exposure to data platforms such as Databricks or ThoughtSpot
● Understanding of RAG pipelines and vector databases

(Hands-on experience across several of the above systems is beneficial)

Commercial & Operational
● Experience operating in a SaaS environment with a strong understanding of core metrics (MRR, churn, retention). Demonstrated ability to deliver measurable ROI from automation initiatives, combined with strong cross-functional stakeholder management.

This is an opportunity to shape how AI is embedded into a scaled SaaS platform — not as experimentation, but as core operational infrastructure.