Our client is a digital services provider operating within the iGaming field. As part of their growth and expansion they are now seeking to recruit an ML Engineer (LLM / Google Cloud) who will be responsible for training and Fine-tuning text models (LLMs), deploying them on Google Cloud, and building automation around these models.
The core mission : take example texts, train the model so that the output strictly follows the required format, and build reliable infrastructure and services that will call this model in production.
Responsibilities
- Analyse business requirements for the desired output format and the logic the model must implement.
- Prepare datasets based on example texts : cleaning, annotation, creating training / validation splits.
- Train and fine-tune LLMs for specific use cases :
configure training parameters;
experiment with prompts, system instructions, input / output formats.Evaluate model quality :design and track metrics;
create test scenarios and A / B experiments;ensure output format consistency and stability.Deploy models to Google Cloud (for example via Vertex AI, Cloud Run, Kubernetes, etc.).Develop services and APIs (REST / gRPC) that expose the model to other systems.Build automations and integrations that call the model :background jobs, queues, event-driven triggers;
integration with internal services and databases.Implement MLOps pipelines :automate training / retraining workflows;
version models and datasets;monitor model performance and quality in production.Document models, pipelines, APIs, and architectural decisions.Requirements
3+ years of software development experience (preferably Python).Hands-on experience with ML / NLP : understanding of models, loss functions, training and validation workflows.Practical experience with at least one ML framework : TensorFlow, PyTorch, Hugging Face, etc.Experience with Google Cloud :Core services (Cloud Storage, IAM, VPC);
ideally Vertex AI, Cloud Run, Pub / Sub or similar.Experience deploying models into production (API services, containerization with Docker, CI / CD).Experience building and integrating REST APIs; confident working with JSON / JSONL, logging, and monitoring.Understanding of how to design reliable and scalable systems (error handling, retries, queues, timeouts).Direct experience with LLMs : prompt engineering, few-shot learning, RAG.Experience with MLOps tools (MLflow, Vertex AI Pipelines or equivalents).Experience with messaging / queue systems (Pub / Sub, Kafka, RabbitMQ) and workflow orchestration (Workflows, Airflow, etc.).Understanding of data security and handling sensitive information, including access control (IAM).