ML Engineer

vor 1 Tag

Berlin, Berlin, Deutschland Triskel Consulting Vollzeit

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).

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