AI Engineer (Commercial Operations)

The AI Engineer (Commercial Operations) is a strategic technical partner to the commercial leadership team, responsible for building AI-powered infrastructure that drives predictable, scalable revenue growth. You will combine advanced engineering with commercial analytics, creating intelligent systems that automate workflows, surface insights and enhance decision-making across Sales, Marketing and Customer Success.

This is a hands-on, technical role for someone who enjoys building production systems, deploying models and solving real business problems with AI. You'll be responsible for both AI engineering (LLMs, ML models, intelligent automation) and foundational commercial analytics, ensuring the business has robust data infrastructure alongside intelligent capabilities that provide genuine competitive advantage.

You will own commercial analytics and the technical architecture of our commercial data ecosystem, whilst building AI solutions that transform how teams operate.

A core expectation is to champion an AI-first approach: using AI and automation not just to make existing processes more efficient, but to help the organisation re-imagine how we work.

Key Responsibilities

AI Engineering & Intelligent Automation

• Design, build and deploy production AI solutions that drive commercial efficiency and unlock new capabilities for GTM teams.
• Develop and maintain machine learning models for revenue forecasting, pipeline prediction, lead scoring, churn risk and expansion opportunity identification.
• Build intelligent automation using LLMs and AI agents to streamline commercial processes (deal analysis, customer sentiment analysis, competitive intelligence, automated reporting).
• Create AI-enhanced analytics capabilities that surface patterns, anomalies and opportunities that traditional methods would miss.
• Prototype and ship AI-enabled workflows rapidly, using tools like Claude, coding assistants and modern AI platforms.
• Evaluate and integrate emerging AI tools, establishing best practices for responsible AI deployment across commercial teams.
• Partner with Sales, Marketing and Customer Success to identify high-impact opportunities for AI-driven improvement.

Revenue Analytics & Insight

• Deliver clean, reliable data and analysis on NRR, GRR, logo retention, expansion and churn, enabling commercial leaders to turn metrics into actionable strategy.
• Build sophisticated analytics combining traditional methods and ML approaches to surface leading indicators and inform proactive decision-making.
• Translate complex datasets into clear narratives, dashboards and recommendations for senior stakeholders and the board.

Commercial Systems & Data Infrastructure

• Own the technical architecture of the commercial data ecosystem, ensuring clean data flow between Salesforce, marketing platforms, product analytics and data warehouses.
• Act as commercial data and systems owner: define data models, governance, definitions and quality standards.
• Drive Salesforce technical excellence where needed: build custom objects, fields, automation and integrations that reflect business logic.
• Build and maintain executive-level dashboards (e.g. in QuickSight or similar) combining traditional metrics with AI-generated insights.
• Ensure seamless integration between product telemetry, CRM data and commercial analytics systems.

Stakeholder Management & Technical Leadership

• Operate as a trusted advisor to the COO, CCO and commercial leadership on AI capabilities and data-driven strategy.
• Work closely with leaders such as the VP Sales, VP Customer and VP Marketing to understand their questions, then design AI tools and analysis that answer them.
• Communicate complex technical concepts clearly to non-technical stakeholders; frame problems and recommend solutions with clarity.
• Build strong cross-functional relationships with Finance and Commercial teams, influencing without authority.
• Support onboarding and enablement of commercial team members on AI-powered tools, dashboards and new ways of working.
• Establish responsible AI practices, addressing bias, explainability and ethical considerations in commercial AI applications.