Especialista de Engenharia de Dados I
Core
Transform analytical models into reliable, observable, and scalable systems from feature pipelines to production monitoring.
Role type
Senior Machine Learning Engineer (MLOps)
Builds
End-to-end ML pipelines, internal ML platforms (feature store, model registry), and inference services.
Domain
Financial services / Machine Learning Engineering
Deliverable
production ML models
Required skills
Python, SQL, distributed processing (Spark), Cloud platforms (AWS/GCP/Azure), Containers (Docker/Kubernetes), Pipeline orchestration (Airflow/Kubeflow/Dagster), MLOps tools (MLflow/SageMaker/Vertex AI/Databricks/Feast), Infrastructure as Code (Terraform), CI/CD, Machine Learning fundamentals, API development (FastAPI/gRPC), Streaming (Kafka/Kinesis), Observability (Prometheus/Grafana/Datadog/Evidently)
Preferred skills
Financial sector experience, Regulated models experience, LLMs/GenAI production experience, Inference optimization (quantization/ONNX/Triton), Open source contributions
Responsibilities
Design and maintain end-to-end ML pipelines; Deploy models at scale in batch, near-real-time, and online modes; Develop and evolve internal ML platforms; Implement model observability and monitoring; Build and maintain CI/CD for ML; Ensure reliability and efficiency of inference services; Collaborate with Data Science to productize experiments; Ensure model governance and compliance; Define and disseminate ML engineering best practices; Contribute to technical architecture and roadmap decisions
Seniority
Senior, hands-on IC