Gambling.com Group (Nasdaq: GAMB) is a multi-award winning provider of marketing and sports data services looking for exceptional talent interested in the fast-paced, high-growth online gambling industry. The company operates a portfolio of renowned websites and brands – including flagship site Gambling.com and sports betting site Bookies.com as well as iGaming-focused sites Casinos.com, BonusFinder.com and Freebets.com.

In addition to its marketing operations, Gambling.com Group provides sports data services through consumer subscription platforms like OddsJam and RotoWire, along with B2B services through OpticOdds. These offerings deliver real-time data, actionable insights, and technology driven tools to both consumers and enterprise partners.

As the first and only online gambling affiliate publicly traded in the U.S., Gambling.com Group has earned recognition as a leader in its field – most recently winning Affiliate of the Year at the 2025 EGR Operator Awards.

Are you ready to take the next step in your career?

Functions and Scope

This is an AI engineering role embedded in a commercial function. The primary output of this role is agents and automated workflows — built, owned, and iterated on by you. Commercial knowledge informs what gets built. The building is the job.

Commercial Operations & Optimisation

• 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.

Workflow Architecture & Process Design

• 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.

Agent & Automation Development

This is the core of the role. You will be building AI agents. Not scoping them for others to build, not advising on them, not reviewing them after the fact — designing, building, deploying, and iterating on them yourself, end to end. If you have not built and shipped an AI agent in a professional context, this role is not the right fit.

• 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.

Quality Assurance & Reporting

• 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.

Key requirements

Essential experience

• 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

Candidates who cannot provide concrete, specific examples of building and shipping agents will not progress past the application stage. This is a builder role,your portfolio of what you have built is the application.

Skills & capabilities

• 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

Behaviours we're looking for

• You have shipped things,your instinct is to build a working version, get it into a live environment, and improve it from there rather than designing indefinitely
• You treat deployed agents as live products,you monitor them, catch when they drift, and fix them without being asked
• You apply structured commercial thinking to what you build,you're not building for technical elegance, you're building to remove manual effort and drive revenue outcomes
• You are honest about the limits of what you've built,you know where your agents are reliable and where they require human oversight, and you design for that honestly
• You communicate proactively with commercial leadership when something isn't working, an underperforming agent that goes unreported is a failure of this role, not just a technical issue

Nice to have

• 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

A note for applicants: If your experience with AI is limited to using consumer tools, prompting chatbots, or working within workflows someone else built, this role is likely a step beyond where you are right now. We are looking for someone who has built agents, shipped them into a live environment, watched them fail, fixed them, and made them better. That experience is the baseline, not the ceiling

Perks & 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

The Analytics Engineering Technical Lead will define and own the modeling architecture, standards, and technical direction within RSI’s cloud-native Data Platform. This role is accountable for ensuring that analytics-ready data is reliable, scalable, and aligned to long-term business needs across growth, operations, and risk.

You will lead the design and governance of core data models in Snowflake and dbt, establishing best practices that enable high-quality, self-service analytics without sacrificing trust or performance. As a hands-on technical leader, you will set standards for modeling, testing, documentation, and deployment while mentoring other Analytics Engineers and raising the overall technical bar of the team.

This role partners closely with Data Engineering, Analytics, and business stakeholders to translate evolving requirements into durable data products. You will help balance speed and governance, prioritize platform investments, and ensure our data foundation scales with the business.

Our modern data stack includes Snowflake, AWS, Python, Airflow, and dbt.

What You'll Do:

• Lead the design, development, and lifecycle management of curated datasets, dbt models, and analytics data marts aligned to business domains and metrics.
• Establish and evolve standards and best practices for dbt modeling, testing, documentation, and code organization to support long-term scalability.
• Ensure analytical datasets are accurate, reliable, and trusted, serving as a single source of truth for business, product, and stakeholders.
• Embed data quality checks, documentation, and governance directly into analytics pipelines.
• Partner closely with Product, Data Engineering and Analytics teams to translate business requirements into well-modeled, performant data products.
• Mentor and support Analytics Engineers through technical guidance, design reviews, and knowledge sharing.
• Continuously improve and modernize analytics workflows by introducing automation, tooling enhancements, and AI-assisted development where appropriate.
• This role includes technical leadership and mentoring responsibilities for the team, including other Analytics Engineers.

What You'll Bring:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related quantitative discipline.
• Technical Leadership: Proven experience defining technical direction, standards, and architectural decisions within analytics engineering or curation teams.
• Data Modeling Expertise: Deep understanding of dbt, data marts, dimensional modeling, and self-service BI enablement.
• Data Engineering Background: 7+ years of experience in data warehousing or development; 5+ years of experience in hands-on data engineering using SQL and Python.
• Cloud Infrastructure: Proficiency with cloud platforms (AWS, GCP, or Azure), including containerization (Docker/Kubernetes).
• Tooling & Orchestration: Experience with tools like dbt, Airflow, S3, REST APIs, and large-scale DWHs (Snowflake, Redshift, BigQuery, etc.).
• Change Management: Skilled at leading cross-functional data initiatives and driving adoption of new tools and standards.
• People Leadership: Experience mentoring and guiding distributed teams across time zones.
• Communication: Strong written and verbal English skills with the ability to collaborate across technical and business functions.
• Able to travel occasionally both domestically and internationally #LI-HYBRID