**About the Company**

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?

**About the Role**

We are looking for a highly skilled Lead AI Solutions Engineer to design, build, and optimize a Retrieval-Augmented Generation (RAG) system that underpins our self-serve analytics data applications. In this pivotal position, you will develop a scalable RAG platform, integrate multiple data sources, and create intuitive data interactions to empower teams across the organization.

**Responsibilities**

- **RAG System Development:**
- Enhance, build new assistants and maintain existing scalable and modular RAG architectures for data retrieval and generation.
- Develop APIs and microservices to integrate RAG capabilities with existing data sources (not only user behaviour data, metadata and semantic layer, but also other sources e.g. HR systems, legal repositories, SharePoint, etc.).
- Manage and optimize LibreChat UI for chatbot interactions and Open WebUI Pipelines for backend model development.
- Strong experience working with large language models (e.g., GPT, Claude family and LLaMA) and embedding models (e.g., Sentence Transformers), including fine-tuning and performance optimization.

**Data Engineering & Integration:**

- Build efficient data pipelines to support LLM-based querying, semantic search, and metadata retrieval.
- Maintain and troubleshoot Airflow pipelines for embedding extraction and document processing.
- Ensure data governance, security, and compliance across all applications.
- Manage Vector Database (PGVector, FAISS, etc.), including indexing and similarity search optimizations.

**Security & Infrastructure Management:**

- Collaborate with Platform Engineering teams to manage apps Kubernetes deployments on Kindred Cloud.
- Ensure security integrations with Azure SSO and SailPoint.
- Work with the Network & Security team to manage configurations for newly created services.

**Testing & Deployment:**

- Develop unit and integration tests to ensure system reliability and performance.
- Validate applications against real-world data and scenarios to assess LLM accuracy and output quality.
- Deploy applications in AWS Cloud environments, ensuring scalability and monitoring in production.
- Use Kubernetes, Docker, Helm and ArgoCD to manage deployments.

**Documentation & Knowledge Transfer:**

- Deliver technical documentation covering system architecture, APIs, and workflows.
- Conduct training sessions and presentations for internal teams to facilitate self-service capabilities and ongoing system enhancements.

**Qualifications**

- 5+ years of experience in AI/ML engineering, data engineering or backend development.
- Strong knowledge of Python (LangChain, LangGraph and agent-based LLM workflows) and FastAPI with the focus on scalability and performance.
- Hands-on experience integrating with LLM providers such as OpenAI and Anthropic for both inference and evaluation workflows.
- Experience with LLM-based architectures, RAG, and NLP techniques.
- Hands-on experience using MLflow or similar tools to track model and prompt evaluation experiments.
- Proficiency in SQL, No-SQL, Redshift and data warehousing concepts.
- Experience integrating structured and unstructured data sources for AI-driven applications.
- Knowledge of dbt, metadata management, and semantic search.
- Cloud deployment experience in AWS or Kindred Cloud.
- Experience with Kubernetes, Terraform, and Helm for deployment management.
- Strong problem-solving skills and ability to work in a fast-paced, agile environment.

**Required Skills**

- 5+ years of experience in AI/ML engineering, data engineering or backend development.
- Strong knowledge of Python (LangChain, LangGraph and agent-based LLM workflows) and FastAPI with the focus on scalability and performance.
- Hands-on experience integrating with LLM providers such as OpenAI and Anthropic for both inference and evaluation workflows.
- Experience with LLM-based architectures, RAG, and NLP techniques.
- Hands-on experience using MLflow or similar tools to track model and prompt evaluation experiments.
- Proficiency in SQL, No-SQL, Redshift and data warehousing concepts.
- Experience integrating structured and unstructured data sources for AI-driven applications.
- Knowledge of dbt, metadata management, and semantic search.
- Cloud deployment experience in AWS or Kindred Cloud.
- Experience with Kubernetes, Terraform, and Helm for deployment management.
- Strong problem-solving skills and ability to work in a fast-paced, agile environment.

**Preferred Skills**

- Experience with Kubernetes, Terraform, and Helm for deployment management.
- Strong problem-solving skills and ability to work in a fast-paced, agile environment.