Senior MLOps/LLMOps Engineer,

At FDJ UNITED, we don’t just follow the game, we reinvent it.

FDJ UNITED is one of Europe’s leading betting and gaming operators, with a vast portfolio of iconic brands and a reputation for technological excellence. With more than 5,000 employees and a presence in around fifteen regulated markets, the Group offers a diversified, responsible range of games, both under exclusive rights and open to competition. We set new standards, proving that entertainment and safety can go hand in hand. Here, you’ll work alongside a team of passionate individuals dedicated to delivering the best and safest entertaining experiences for our customers every day.

We’re looking for bold people who are eager to succeed and ready to level-up the game. If you thrive on innovation, embrace challenges, and want to make a real impact at all levels, FDJ UNITED is your playing field.

Join us in shaping the future of gaming. Are you ready to LEVEL-UP THE GAME?

The Role

As a Senior MLOps/LLMOps Engineer, you will be at the forefront of building and scaling our AI/ML infrastructure, bridging the gap between cutting-edge large language models and production-ready systems. You will play a pivotal role in designing, deploying, and operating the platforms that power our AI-driven products, working at the intersection of DevOps, MLOps, and emerging LLM technologies.

In this role, you’ll architect robust, scalable infrastructure for deploying and monitoring large language models (LLMs) such as GPT and Claude-family models in AWS Bedrock & AWS AI Foundry, while ensuring security, observability, and reliability across multi-tenant ML workloads. You will collaborate closely with data scientists, ML engineers, platform teams, and product stakeholders to create seamless, self-serve experiences that accelerate AI innovation across the organization.

This is a hands-on leadership role that blends strategic thinking with deep technical execution. You’ll own the end-to-end ML platform lifecycle; from infrastructure provisioning and CI/CD automation to model deployment, monitoring, and cost optimization. As a senior technical leader, you’ll champion best practices, mentor team members, and drive a culture of continuous improvement, experimentation, and operational excellence.

Key Responsibilities

Platform Infrastructure & Deployment

Run and evolve our ML/LLM compute infrastructure on Kubernetes/EKS (CPU/GPU) for multi-tenant workloads, ensuring portability across AWS/Azure AI Foundry regions with region-aware scheduling, cross-region data access, and artifact management
Engage with platform and infrastructure teams to provision and maintain access to cloud environments (AWS, Azure), ensuring seamless integration with existing systems
Setup and maintain deployment workflows for LLM-powered applications, handling environment-specific configurations across development, staging/UAT, and production
Build and operate GitOps-native delivery pipelines using GitLab CI, Jenkins, ArgoCD, Helm, and FluxCD to enable fast, safe rollouts and automated rollbacks

LLM Operations & Optimization

Deploy, scale, and optimize large language models (GPT, Claude, and similar) with deep consideration for prompt engineering, latency/performance tradeoffs, and cost efficiency
Operate and maintain Argo Workflows as reliable, self-serve orchestration platforms for data preparation, model training, evaluation, and large-scale batch compute
Implement and evaluate models using AI Observability frameworks to track model performance, drift, and quality in production

CI/CD & Infrastructure as Code

Design and maintain robust CI/CD pipelines with isolated development, staging, and production environments to support safe iteration, reproducibility, and full lifecycle observability
Implement Infrastructure as Code (IaC) using Terraform, CloudFormation, and Helm to automate provisioning, configuration, and scaling of cloud resources
Manage container orchestration, secrets management (e.g., AWS Secrets Manager), and secure deployment practices across all environments

Observability, Monitoring & Reliability

Set up and analyze comprehensive observability stacks using Prometheus/Grafana and Splunk to monitor model health, infrastructure performance, and system reliability
Support system monitoring for health, usage, and cost across AWS and Azure environments, including CloudWatch, ELK Stack, and custom alerting solutions
Implement sensible alerting strategies to proactively detect and resolve incidents, minimizing downtime and ensuring high availability
Proactively troubleshoot production issues, manage release cycles, and provide on-call support as necessary

Data Platform & Experiment Reproducibility

Design and maintain a modern data platform built on Apache Iceberg to enable experiment reproducibility, data lineage tracking, and automated governance
Build data pipelines with strong principles of idempotency, retries, backfills, and reproducibility to support ML workflows
Collaborate with data engineers to ensure seamless integration between data ingestion, transformation, and model training processes

Developer Experience & Enablement

Own developer experience by creating intuitive APIs, CLIs, and minimal UIs that enable engineers and data scientists to self-serve infrastructure and deployment needs
Develop comprehensive, modular documentation covering system architecture, deployment processes, model usage guidelines, onboarding playbooks, and operational runbooks
Treat the ML platform as a product: engage with internal users (engineers, data scientists), gather feedback, remove friction points, and continuously improve usability
Create reusable templates, standards, and best practices to ensure maintainability, consistency, and scalability across teams

Architecture, Security & Governance

Define and refine platform architecture with a focus on scalability, security, and compliance with organizational and regulatory standards
Engage in security approval conversations, ensuring that infrastructure, deployments, and data handling meet security and governance requirements
Implement FinOps best practices, including cost attribution, budget monitoring, and optimization strategies for multi-tenant ML infrastructure
Champion a culture of continuous integration, continuous delivery, and continuous improvement across engineering teams

Skills, Knowledge, and Experience

Essential Experience

8+ years of experience in DevOps, Platform Engineering, or Site Reliability Engineering, with at least 2+ years focused on MLOps/LLMOps
Deep hands-on expertise with AWS services, including Bedrock, S3, EC2, EKS, RDS/PostgreSQL, ECR, IAM, Lambda, Step Functions, and CloudWatch
Production experience managing Kubernetes workloads in EKS, including GPU workloads, autoscaling, resource quotas, and multi-tenant configurations
Proficient in container orchestration (Docker, Kubernetes), secrets management, and implementing GitOps-style deployments using Jenkins, ArgoCD, FluxCD, or similar tools
Practical understanding of deploying and scaling LLMs (e.g., GPT and Claude-family models), including prompt engineering, latency/performance tradeoffs, and model evaluation
Strong programming skills in Python (FastAPI, Django, Pydantic, boto3, Pandas, NumPy) with solid computer science fundamentals (performance, concurrency, data structures)
Working knowledge of Machine Learning techniques and frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
Experience building and operating data pipelines with principles of idempotency, retries, backfills, and reproducibility
Expertise in Infrastructure as Code (IaC) using Terraform, CloudFormation, and Helm
Proven track record designing and maintaining CI/CD pipelines with GitLab CI, Jenkins, or similar tools
Observability experience with Prometheus/Grafana, Splunk, Datadog, Loki/Promtail, OpenTelemetry, and Sentry, including implementing sensible alerting strategies
Strong grasp of networking, security concepts, and Linux systems administration
Excellent communication skills with ability to collaborate across development, QA, operations, and product teams
Self-motivated, proactive, with a strong sense of ownership and a passion for removing friction and improving developer experience

Nice to Have

Experience with distributed compute frameworks such as Dask, Spark, or Ray
Familiarity with NVIDIA Triton, TorchServe, or other inference servers
Experience with ML experiment tracking platforms like Weights & Biases, MLflow, or Kubeflow
FinOps best practices and cost attribution strategies for multi-tenant ML infrastructure
Exposure to multi-region and multi-cloud designs, including dataset replication strategies, compute placement, and latency optimization
Experience with LakeFS, Apache Iceberg, or Delta Lake for data versioning and lakehouse architectures
Knowledge of data transformation tools such as DBT
Experience with data pipeline orchestration tools like Airflow or Prefect
Familiarity with Snowflake or other cloud data warehouses
Understanding of responsible AI practices, model governance, and compliance frameworks

Our Way Of Working

Our world is hybrid.

A career is not a sprint. It’s a marathon. One of the perks of joining us is that we value you as a person first. Our hybrid world allows you to focus on your goals and responsibilities and lets you self-organize to improve your deliveries and get the work done in your own way.

Application Process

We believe talent knows no boundaries. Our hiring process focuses solely on your skills, experience, and potential to contribute to our team. We welcome applicants from all backgrounds and evaluate each candidate based on merit, regardless of personal characteristics such as age, gender, origin, religion, sexual orientation, neurodiversity, or disability.

Why Join FDJ UNITED?

Work on cutting-edge AI/ML technologies at scale in a regulated, high-stakes industry
Technical leadership opportunities with visibility across the organization
Collaborate with world-class engineers, data scientists, and product teams
Influence the architecture and strategy of our AI platform from the ground up
Continuous learning environment with access to the latest tools, technologies, and practices

Our Way Of Working

Our world is hybrid.

A career is not a sprint. It’s a marathon. One of the perks of joining us is that we value you as a person first. Our hybrid world allows you to focus on your goals and responsibilities and lets you self-organise to improve your deliveries and get the work done in your own way.

Application Process

We believe talent knows no boundaries. Our hiring process focuses solely on your skills, experience, and potential to contribute to our team. We welcome applicants from all backgrounds and evaluate each candidate based on merit, regardless of personal characteristics as the age, gender, origin, religion, sexual orientation, neurodiversity or disability.

Details
Hybrid
London, Stockholm
Full Time Permanent
TEC2682
Location
London
Stockholm
Kindred House, 17-25 Hartfield Road, Wimbledon, London, United Kingdom, SW19 3SE

Benefits
Well-being allowance
Learning and development opportunities
Inclusion networks
Charity days
Long service awards
Social events and activites
Private medical insurance
Life assurance and income protection
Employee Assistance Programme
Pension
Meet the recruiter
Prachi Arya

[email protected]