Senior Machine Learning Engineer

Role overview

Responsibilities

• check_circle Design, train and deploy scalable machine learning models using Getnet data to solve high-impact payment and business challenges across geographies.
• check_circle Build and maintain end-to-end machine learning pipelines, from exploration and feature engineering through scoring, deployment and monitoring.
• check_circle Lead the technical design and delivery of proofs of concept and new AI/ML initiatives within Getnet Payments' AI Lab.
• check_circle Drive decisions on model architecture, experimentation and deployment, balancing performance, scalability, explainability and maintainability.
• check_circle Partner with business, product and cross-functional teams to turn prioritized opportunities into production-ready data solutions.
• check_circle Strengthen engineering quality through clean code, automated testing, documentation, version control, CI/CD and reusable standards.
• check_circle Monitor production models for drift, data quality, performance and reliability, using alerts and dashboards to guide action.
• check_circle Mentor junior colleagues and communicate complex findings clearly to non-technical stakeholders across markets.

Basic qualifications

• 5+ years of professional experience in Machine Learning Engineering, Data Science or Applied Research roles. (Required)
• Proven experience building, deploying and operating end-to-end machine learning solutions on tabular data at scale in production cloud environments. (Required)
• Experience leading technical decisions, proofs of concept or AI/ML initiatives and supporting junior team members. (Required)
• Experience working in agile, cross-functional teams across multiple markets or geographies. (Required)
• Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Physics, Engineering or a related quantitative field. (Required)
• English, professional proficiency. (Required)

Preferred qualifications

• Experience in fintech, payments, banking or e-commerce. (Preferred)
• PhD in a related quantitative field. (Preferred)
• LLM-based AI agents using LangChain, LangGraph, OpenAI Agents SDK, CrewAI or Agno, with knowledge of MCP, LangFuse, MLflow Tracing, multi-agent orchestration, RAG, tool use and domain-specific AI models. (Preferred)
• AWS services, including SageMaker, S3, Bedrock, EKS, Lambda, Step Functions and Glue, and experience working across Azure and AWS. (Preferred)

Benefits

• check_circle We care for your well-being, so you will have health insurance, life insurance, dental insurance, annual health care, and access to Wellhub.
• check_circle We promote flexibility with a hybrid and collaborative model; the selected candidate must be ready to work 60% on-site.
• check_circle We aim to boost your growth with access to self-development programs.
• check_circle We aim to make your workdays more accessible, so you will have transportation allowance and meal voucher/food voucher.

About the company