We are looking for an experienced Machine Learning Engineer to lead the development, deployment, and scaling of intelligent models on our infrastructure. As a Senior ML Engineer, you will serve as the bridge between data science and software engineering, ensuring that our AI solutions are not only accurate but also resilient, scalable, and efficient within the Google Cloud ecosystem. Your challenge will be to transform prototypes into high-impact production systems, applying best practices in MLOps to support the full model lifecycle. **Responsibilities and Duties** **ML Systems Architecture:** Design and implement scalable architectures for model training and inference. **Data and ML Pipelines:** Build and optimize CI/CD pipelines for Machine Learning (MLOps), automating everything from data processing to deployment and monitoring. **Performance Optimization:** Ensure low latency and high availability of models served via APIs or batch processing. **Multidisciplinary Collaboration:** Work closely with Data Scientists to translate business requirements into technical specifications and ensure experiment reproducibility. **Technical Mentorship:** Guide junior team members, promoting best practices in coding (Clean Code), testing, and technical documentation. **Infrastructure Management:** Manage and optimize costs of computational resources (GPUs/TPUs) and managed services on GCP. **Requirements and Qualifications** **Proven Experience:** Demonstrated track record implementing ML models in production environments (senior level). **GCP Proficiency:** Deep experience with the Google Cloud ecosystem, especially: * **Vertex AI** (Pipelines, Model Registry, Feature Store, Endpoint). * **BigQuery & BigQuery ML**. * **Dataflow** or **Dataproc** for large-scale data processing. * **Cloud Storage** and **Artifact Registry**. **Languages and Frameworks:** Fluency in **Python**, with expertise in libraries such as TensorFlow, PyTorch, or Scikit-learn. **Software Engineering:** Experience with Docker, Kubernetes (GKE), and CI/CD tools (GitHub Actions, GitLab CI, or Cloud Build). **Databases:** Advanced SQL knowledge and familiarity with NoSQL databases. **MLOps:** Strong understanding of model monitoring (drift detection) and data versioning (DVC) and model versioning (MLflow). **Additional Information** **Certifications:** Hold the *Google Professional Machine Learning Engineer* certification. **Generative AI:** Experience with Large Language Models (LLMs), frameworks such as LangChain/LlamaIndex, and use of the **Model Garden** in Vertex AI. **Infrastructure as Code (IaC):** Knowledge of Terraform for provisioning resources on GCP. **Advanced Analytics:** Experience with distributed hyperparameter tuning and large-scale distributed processing. **OUR STORY** La Moda Group is a vibrant organization, creating brands that launch with strong desire and numerical success. We develop innovative solutions capable of connecting people’s desires with fashion and behavioral trends. Our story began in 1986 with a small family business founded by Arcílio and Vera Olivo. After 20 years of experience in children’s apparel manufacturing, **Lança Perfume** was launched in 2006. The brand’s exponential growth and market relevance elevated La Moda Group to a new level, enabling the creation of new brands targeting different audiences: **My Favorite** in 2016 and **Lança Perfume Easy** in 2021. La Moda Group’s purpose is to bring emotion to people’s lives and currently employs approximately 1,000 people. The Group’s headquarters is located in Criciúma/SC, with facilities in Nova Veneza/SC, two direct-sales showrooms in São Paulo/SP and Goiânia/GO, and 20 company-owned stores nationwide. **OUR LA MODA WAY** Our values are our non-negotiable principles—a genuine choice. We believe we can perform the ordinary in a completely different and extraordinary way. Our values guide our actions and inform our decisions. They are: Learn and Teach; Dream Big; Challenge the Status Quo; Collaborate; Surprise.