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About the role
We are looking for 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.
Key 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).
Nice to have
- 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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