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).