Senior Machine Learning Engineer

Machine Learning Engineer connecting prototype to production at Leega. Focused on designing and building ML engineering for real-time pricing engine.

Posted 6/10/2026full-timeRemote • 🇧🇷 BrazilSeniorWebsite

Tools & technologies

DockerPythonRayRedis

Key responsibilities & impact

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.

What you’ll need

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.

Comp & perks

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Hard Skills & Tools

machine learning engineeringinference servingfeature engineeringdistributed trainingoptimizationreinforcement learningPythonAPIstestingclean code

Soft Skills

technical leadershipmentoringcollaboration