You will play a key role in the AI/ML strategy for the Operations area, collaborating to help the team fulfill its role as owner of the company's Revenue Strategy.
Your work will be to develop robust, scalable solutions that enable Marketing, Sales, and Customer Success operations to become more data-driven, increasing efficiency and impact on business results.
Build training pipelines for Machine Learning and Deep Learning models (supervised, unsupervised, and language models
LLMs) to solve high-impact problems.
Implement and maintain robust MLOps pipelines (CI/CD, versioning, model drift and data drift monitoring) to automate the full model lifecycle.
Integrate AI models with front-end, back-end, and operational systems, ensuring high availability, low latency, and scalability.
Work collaboratively with Data Engineers to ensure the supply of high-quality features and optimized infrastructure for training and inference.
Monitor model performance in production, ensuring performance and retraining when necessary, and propose innovative architectural solutions in MLOps and AI.

Degree in IT, Computer Science, Computer Engineering, Statistics, or related fields.
Strong experience in Machine Learning Engineering, with a focus on operationalizing (deploying) models to production.
Proven experience with the full lifecycle (MLOps) of at least one Deep Learning model or LLM.
Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn).
Solid experience with Google Cloud Platform (GCP), including BigQuery, Cloud Functions/Cloud Run, and Compute Engine.
Proficiency in MLOps (CI/CD, model and feature versioning).
Experience with model serving and containers.

Senior Artificial Intelligence Engineer

Key skills