The Machine Learning Engineer is responsible for designing, building, deploying, and maintaining machine learning models and AI‑driven systems that enhance the Sigma Groups global event experiences, including major iGaming events. This role bridges data science and engineering, ensuring that AI solutions are production‑ready, scalable, secure, and aligned with business needs.

The ideal candidate combines strong software engineering skills with deep knowledge of machine learning, data pipelines, and modern AI tooling. They will play a key role in enabling the organisation’s AI strategy, supporting both internal automation and customer‑facing AI‑powered products and services.

Key Responsibilities

ML Model Development & Deployment

Builds, trains, optimises, and deploy machine learning models for use cases such as:
- personalised event recommendations
- attendee behaviour prediction
- exhibitor and sponsor performance forecasting
- churn and retention modelling
- anomaly detection for event operations
Implement smodel monitoring, retraining pipelines, and performance optimisation.

AI Tooling & Platform Integration

Introduces and integrates AI tooling that enhances engineering and operational workflows, including:
- automated model evaluation
- AI‑assisted feature engineering
- intelligent monitoring and diagnostics
Works with platform and data teams to ensure infrastructure supports scalable ML workloads.
Contributes to the development of AI‑powered internal tools and customer‑facing features.

Data Pipeline Engineering

Collaborates with data engineers to build and maintain robust data pipelines for model training and inference.
Ensures data quality, consistency, and availability across event systems, CRM platforms, mobile apps, and iGaming‑related tools.
Implements feature stores, data validation frameworks, and automated data‑quality checks.

Events & iGaming AI Applications

Develops ML solutions tailored to the unique dynamics of live events and iGaming audiences.
Supports real‑time inference systems that handle high‑traffic spikes during major events.
Builds models that enhance attendee engagement, exhibitor value, and partner insights.

Security, Compliance & Responsible AI

Ensures ML systems comply with data‑privacy regulations and responsible‑gaming requirements where applicable.
Implements responsible‑AI practices, including bias detection, explainability, and ethical model usage.
Collaborates with security teams to embed secure‑by‑design principles into ML workflows.

Collaboration & Cross‑Functional Support

Works closely with data scientists, data engineers, product managers, and platform teams to deliver end‑to‑end AI solutions.
Translates business requirements into technical ML specifications.
Communicates model performance, insights, and trade‑offs to technical and non‑technical stakeholders.

Qualifications

Key Skills & Competencies

Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit‑learn)
Experience with cloud‑based ML platforms and MLOps tools
Strong understanding of data engineering, distributed systems, and real‑time processing
Familiarity with AI‑assisted development tools and emerging ML technologies
Excellent problem‑solving and analytical skills
Ability to work effectively in cross‑functional teams
Experience with event‑driven or iGaming data is a plus

Preferred Experience

Educated to degree level in a numerate or technical discipline, Masters preferred.
5-7+ years of technical experience in machine learning, AI engineering, or related fields
1-2+ years of management or mentorship experience, such as leading ML projects or guiding junior engineers
Proven track record of deploying ML models into production environments
Experience supporting AI‑enabled products or automation initiatives
Background working with event data, digital engagement metrics, or iGaming systems
Experience with MLOps, CI/CD for ML, and scalable cloud architectures