Note: The job is a remote job and is open to candidates in USA. Gambling.com Group is a multi-award winning provider of marketing and sports data services in the online gambling industry. They are seeking a Commercial Operations Engineer to lead the development of AI agents and automated workflows that optimize commercial processes and drive revenue across all channels.
Responsibilities
- Support the commercial function in commercially optimising vertical site performance to drive revenue across all channels — doing so primarily by identifying where AI agents and automated workflows can replace or accelerate manual commercial processes, and then building them
- Act as the primary operational owner of commercial operational processes across all verticals, which in practice means owning the AI and automation layer that powers those processes, not just the processes themselves
- Conduct ongoing competitor analysis, develop new commercial process and agentic workflow ideas, and collaborate with product and marketing teams to drive operational improvements, using AI tooling to run analysis at scale, generate workflow concepts, and prototype solutions faster than traditional approaches allow
- Collaborate with commercial leadership to set, manage, and monitor BAU processes, KPIs, and OKRs across the commercial operations function, building AI-assisted reporting and monitoring infrastructure so that performance visibility is automated, not manual
- Leverage AI to enhance internal software and tooling to build effective, transparent internal processes across commercial operations, this is not a peripheral responsibility, it is the core mechanism through which this role delivers value. You are expected to be actively building, not just recommending
- Identify inefficiencies and bottlenecks across commercial operations and design structured solutions, your default answer to an inefficiency is an agent or an automated workflow, with manual process redesign as the fallback where automation is not yet viable
- Own the design and deployment of operational workflows that reduce manual effort and improve output quality and consistency across the commercial team, from initial architecture through to live deployment, with measurable reduction in human effort as the success metric
- Develop and maintain playbooks and performance standards for all commercial workflows, including documentation of how each AI agent or automated workflow is expected to behave, what good output looks like, and where human review is required
- Coordinate with sales and commercial leadership on operational prioritisation and workflow sequencing, translating commercial priorities into a build roadmap for agents and automations, and managing that roadmap actively
- Lead the identification and scoping of agentic automation opportunities across commercial operations, this means proactively finding problems worth solving with AI, not waiting to be briefed. You are expected to surface opportunities, make the case for them, and own the build
- Own and build the full development lifecycle for commercial agents across all commercial operations, from problem definition and workflow brief through to prompt architecture, tool integration, testing, deployment, and post-launch iteration. You are the builder, not the commissioner
- Ensure quality, accuracy, and commercial alignment of all automated outputs, establishing QA standards and performance benchmarks for every agent you deploy, and holding yourself accountable for what those agents produce at scale
- Iterate on agent performance post-deployment based on commercial data, stakeholder feedback, and operational signals, treating deployed agents as live products that require ongoing maintenance and improvement, not finished projects
- Establish and oversee QA processes across all commercial operations workflows, including designing the human review layer that sits alongside AI-driven outputs, defining what requires sign-off before going live, and building the infrastructure to enforce that consistently
- Generate, analyse, and present operational KPI reports to senior stakeholders, using AI-assisted tooling to automate data aggregation and surface anomalies, so that your contribution at the reporting layer is insight and recommendation, not manual data pulling
- Monitor workflow and agent performance, surfacing actionable recommendations to commercial leadership on a regular basis, including honest assessment of where agents are underperforming, where outputs are drifting from expected standards, and what needs to be rebuilt or retired
- Operate as the internal expert on AI-enabled tools including Claude, Lovable, and others, not as a power user, but as the person who understands these platforms deeply enough to build production-grade commercial workflows on top of them, apply rigorous judgement to their outputs, and teach others how to work alongside them effectively
Skills
- Experience in a commercial operations, revenue operations, or similar ops role, ideally within a digital media, affiliate, or multi-vertical online business
- You have built AI agents and shipped them into a live production environment, this is the baseline requirement for this role, not an aspiration. If your experience is limited to using AI tools, prompting chatbots, or working within workflows someone else built, this role is likely beyond where you are right now
- Demonstrable, end-to-end ownership of the agent development lifecycle, from identifying the commercial problem, writing the workflow brief, designing the prompt architecture, integrating tools, testing, deploying, and iterating post-launch. You were the builder, not the person who commissioned or reviewed someone else's build
- Experience with AI platforms and tools such as Claude, Lovable, n8n, or equivalent, with specific, named examples of what you built on them, what it did, and how you measured whether it was working
- Track record of designing and documenting operational processes, playbooks, and performance standards, including documentation of how agents are expected to behave, what good output looks like, and where human review is required
- Experience establishing QA frameworks for AI-generated or automated outputs, setting performance benchmarks, monitoring for drift or failure, and iterating on agent behaviour based on real-world results
- Comfortable owning and reporting on KPIs and OKRs, with experience building automated reporting infrastructure rather than pulling data manually
- You must be able to demonstrate, with specific examples, that you have built and deployed at least one AI agent in a professional context and can walk through exactly what it did, how you built it, where it failed, and what you did to fix it
- Designed prompt architecture for a production workflow, not just written prompts, but structured them as part of a repeatable, scalable agent build
- Integrated AI agents with external tools, platforms, or data sources as part of a live commercial or operational workflow
- Identified a commercial or operational problem, translated it into an agent build brief, built the solution yourself, and measured the outcome
- Established and enforced QA standards for automated outputs, including defining what human review is required and building the process to ensure it happens consistently
- Iterated on a deployed agent based on performance data or stakeholder feedback, treating it as a live product, not a finished project
- Prompt architecture and agent design, able to structure AI instructions for production-grade reliability, not just one-off outputs
- Tool integration, comfortable connecting AI agents to external platforms, APIs, data sources, and internal tooling as part of a live workflow build
- Commercial judgement, able to prioritise build decisions based on revenue impact and business context, identifying which automation opportunities are worth the investment
- Process design instinct, able to map a commercial problem, identify the automation opportunity within it, and design a structured agent-based solution
- Strong cross-functional communication, able to translate between commercial stakeholders who define the problem and technical counterparts who support the infrastructure
- Rigorous approach to AI output review, understanding that what an agent produces at scale is your responsibility, and building QA into every deployment accordingly
- Experience in the affiliate, igaming, media, or performance marketing sectors
- Familiarity with competitor analysis methodologies in a commercial context
- Exposure to product or engineering teams in a workflow or tooling capacity
- Experience building agents that operate across multiple tools or platforms simultaneously, multi-step, multi-tool agent architectures rather than single-task automations
Benefits
- Comprehensive private Healthcare Insurance
- Flexible work environment and home office available
- Home office allowance
- Gym & Leisure Allowance
- All the hardware and software you need to be successful
- Regular company events and social outings, activities, Spot Awards and a Monthly Social Club
- Access to courses for Personal and Career Development
- Company Paid Volunteer Day
Company Overview
Gambling.com Group is a multi-award winning provider of digital marketing services for the global, regulated online gambling industry. It was founded in 2006, and is headquartered in Dublin, Dublin, IRL, with a workforce of 501-1000 employees. Its website is https://www.gambling.com/corporate.