ML Engineer (LLM Google Cloud)

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).