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).