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Leega

Inteligência, Inovação e Tecnologia.

Senior Machine Learning Engineer

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteSeniorTeam 201-500Since 2010H1B No SponsorCompany SiteLinkedIn

Location

Brazil

Posted

2 days ago

Salary

0

Seniority

Senior

Job Description

Senior Machine Learning Engineer

Leega

• You are the one who connects the prototype to production. • You will design and build the ML engineering for the pricing engine — the inference serving, training pipelines, and feature engineering — so that complex models run in real time with low latency on Ray. • Focus on modeling and ML code; the platform and runtime are owned by the MLOps/Platform team, with whom you will work closely. • Inference serving — design the pipeline of chained models on Ray Serve — model composition, low latency, and update strategies. • Distributed training — build training pipelines (Ray Train/Data), HPO (Ray Tune), and per-tenant trained models with resilient checkpointing. • Feature engineering — define and materialize features in the feature store (Feast/Redis), ensuring consistency between training and production. • Optimization and RL — implement and optimize the optimization components (linear programming) and offline RL of the pricing pipeline. • Model quality — monitor drift from a modeling perspective, validate versions, and produce explainability (SHAP) — in partnership with MLOps. • Technical leadership — serve as a reference, mentor, and define with the team what is feasible and scalable. • You perform handoffs with data scientists, receive data from data engineers, and deliver to the MLOps/Platform team for deployment and operation.

Job 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 (model registry, deployment).
  • Comfortable with AI-assisted development (e.g., Claude Code).
  • Ray ecosystem (Serve, Train, Tune, RLlib) — strong plus.
  • Feature stores (Feast) and low-latency serving with Redis at scale.
  • Optimization/solvers (Gurobi, HiGHS) or real-time revenue management; offline RL.
  • Generative AI serving (vLLM, LiteLLM) and multi-tenant architectures.

Benefits

  • Remote work
  • Project duration: 6 months, with possibility of extension or internal hire.

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