We are seeking a **Senior Data Scientist** with strong expertise in **MLOps and Platform Engineering**, capable of leading AI initiatives at scale, designing data pipelines, training machine learning models, and building robust platforms for model deployment, monitoring, and governance. This professional will ensure AI solutions are delivered with high performance, security, reproducibility, and scalability, collaborating closely with Data, Engineering, Product, and Architecture teams. **Responsibilities** Develop, train, and validate machine learning and advanced AI models. Design and implement **MLOps pipelines** to automate model training, versioning, testing, deployment, and monitoring. Build and evolve the **ML platform** (feature store, model registry, pipelines, automations, and integrations). Define scalable architectures for data and model processing in production. Collaborate with cross-functional teams to identify use cases and translate business problems into data-driven solutions. Ensure best practices in governance, including experiment tracking, traceability, and model quality control. Conduct benchmarks, tuning, evaluation, and periodic re-evaluation of model performance (drift, accuracy, ROI). Support less experienced data scientists and engineers in using the platform and adopting MLOps standards. **Technical Requirements** **Required:** Advanced proficiency in **Python**, and ML libraries (Scikit-learn, XGBoost, TensorFlow, or PyTorch). Practical experience with **MLOps** on cloud platforms (AWS, GCP, or Azure). Solid knowledge of **CI/CD**, containers, and orchestration (Docker, Kubernetes). Experience with **MLFlow**, **Kubeflow**, **SageMaker**, **Vertex AI**, or equivalent tools. Understanding of data architecture: ETL/ELT, Data Lake, Data Warehouse. Experience with logging, monitoring, and observability tools (Prometheus, Grafana, CloudWatch, Datadog). Strong ability to design data and model pipelines. **Desirable:** Experience with Feature Stores (Feast, Tecton). Experience with LLMOps (generative models, LLM pipelines, prompt evaluation and monitoring). Familiarity with engineering practices: automated testing, version control, code review. Knowledge of advanced statistical modeling and experimentation (A/B testing). Cloud certifications (AWS, GCP, or Azure). **Soft Skills** Analytical mindset with results orientation. Clear communication with both technical and non-technical stakeholders. Ability to technically lead complex projects. Proactive identification of platform improvements. Collaboration and team spirit.