Senior Machine Learning Engineer

Job not on LinkedIn

🕒 June 10

🇧🇷 Brazil – Remote

⏳ Contract/Temporary

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 34%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Leega

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2010

💼 Consulting

📣 Marketing

🔌 API

Consulting • Marketing • API

Leega is a leading technology solutions provider in Latin America, specializing in data analytics and cloud solutions. As the first company in the region certified by Google Cloud for Data Analytics, Leega offers a range of services including application development, machine learning, and risk management analytics. The firm partners with major cloud services such as AWS and Microsoft Azure to help businesses enhance their data management and transition effectively to the cloud, ultimately driving digital transformation and innovation.

📋 Description

• Design and build the ML engineering for the pricing engine • Develop inference serving, training pipelines, and feature engineering on Ray • Design chained model pipelines on Ray Serve, including composition, low latency, and update strategies • Build training pipelines with Ray Train/Data, HPO with Ray Tune, and tenant-specific trained models with resilient checkpointing • Define and materialize features in the Feast/Redis feature store, ensuring consistency between training and production • Implement and optimize linear programming and offline RL components of the pricing pipeline • Monitor modeling-related drift, validate versions, and deliver explainability using SHAP in partnership with MLOps • Serve as a technical reference, mentor team members, and determine what is feasible and scalable • Coordinate handoffs with data scientists, receive data from data engineers, and deliver to the MLOps/Platform team for deployment and operations

🎯 Requirements

• Proven experience putting ML models into production • Python and strong Software Engineering fundamentals (APIs, testing, clean code) • Inference serving and optimization for low latency • Familiarity with containers (Docker) and MLOps workflows (registry, deployment) • Comfortable with AI-assisted development (Claude Code) • Ray (Serve, Train, Tune, Data, RLlib) • MLflow, Feast + Redis • Linear programming (Gurobi, HiGHS) • Offline RL • Iceberg data reading • Generative AI serving (vLLM, LiteLLM) and multi-tenant architectures are a plus

🏖️ Benefits

• Ongoing training and continuous investment in employee development • Remote work • Six-month project, with the possibility of extension or transition to a permanent internal role