Senior Machine Learning Engineer at Itaú, driving innovation with data and AI solutions. Collaborating across teams to implement robust machine learning architectures and ensure scalable deployments.
About the role
- Machine Learning Developer building robust data pipelines and scale ML systems at Morgan Stanley. Collaborating with Data Scientists and Engineers to transform big data into actionable insights.
Responsibilities
- Design, develop, and maintain data pipelines for ingesting, transforming, and analyzing large-scale trading datasets.
- Implement and productionize advanced analytics and machine learning workflows—leveraging Spark, Python and modern big data technologies.
- Collaborate closely with Data Scientists and Engineering team members to operationalize models and automation.
- Optimize code and data workflows for performance, scalability, and reliability.
- Leverage MLOps components, automate feature engineering as part of robust, production ML workflows.
- Write clean, well-tested, well-documented code and participate in code reviews.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field.
- 5+ years of relevant professional experience in software development, data engineering, or machine learning applications.
- Programming: Proficiency with Python (ex. Pandas, NumPy, Jupyter Notebooks), SQL for data engineering and analytics.
- Exposure and/or interest in the latest technologies in machine learning, AI (Modern AI: Generative AI, Multi-Agent Systems, MCP, Agentic AI architectures) and big data; advocate for best practices across the team.
- Highly Desirable Skills (Any of the following): Experience with Java, Scala (e.g. Spark)
- Big Data & ML Platforms: Spark, Databricks, Snowflake, Azure
- Machine Learning & MLOps: Feature stores, model registry, MLOps frameworks, continuous model monitoring and retraining, scalable model deployment
- Front-end Development: React (for building dashboards or UI integrations)
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Professional development opportunities
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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