Machine Learning Engineer

Onebridge, a Marlabs Company, is a global AI and Data Analytics Consulting Firm that empowers organizations worldwide to drive better outcomes through data and technology. Since 2005, we have partnered with some of the largest healthcare, life sciences, financial services, and government entities across the globe. We have an exciting opportunity for a highly skilled Machine Learning Engineer to join our innovative and dynamic team.

Employment Type: Full Time

Location: Brazil - Remote

Industry: IT & Services.

This position requires advanced communication skills in English, both written and verbal.

Machine Learning Engineer | About You

As a Machine Learning Engineer, you are responsible for turning complex data and business challenges into scalable, high‑impact AI solutions. You thrive in environments where experimentation meets engineering rigor, and you take pride in building systems that work reliably in the real world. You are the kind of person who enjoys staying ahead of industry trends—especially in LLMs and generative AI—and bringing that knowledge into practical, production-ready workflows. You’re excited by the opportunity to shape modern ML pipelines end‑to‑end, collaborate deeply with cross-functional teams, and continuously refine how AI drives value across an organization.

Machine Learning Engineer | Day-to-Day

• Develop and maintain scalable, reproducible machine learning pipelines across preprocessing, training, deployment, and monitoring.
• Build and optimize LLM-based solutions, including RAG approaches, embeddings, and semantic search capabilities.
• Collaborate closely with product and engineering teams to translate business needs into strong ML/AI system designs.
• Prototype and deploy ML and deep learning models for transformation, ranking, prediction, and other applied use cases.
• Design native workflows on AWS using Lambda, Step Functions, SageMaker, Bedrock, AgentCore, and related services.
• Implement observability and monitoring tools to ensure reliability, stability, and compliance of models in production.

Machine Learning Engineer | Skills & Experience

• 5+ years of experience in Machine Learning, with a proven track record deploying end‑to‑end ML/AI systems in production.
• Hands-on experience with AWS AI/ML services including SageMaker, Bedrock, Lambda, and Step Functions, along with MLOps best practices.
• Strong knowledge of LLMs, RAG architectures, embeddings, and vector databases.
• Proficiency in Python and ML/DL frameworks such as PyTorch and scikit‑learn, familiarity with transformer-based models.
• Solid understanding of model deployment, containerization, and monitoring.
• Experience with data engineering for ML workflows (feature pipelines, schema versioning, data quality controls).
• Advanced English proficiency for communication with a global, distributed team.