Senior Machine Learning Engineer

Job not on LinkedIn

🕒 June 10

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

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

• 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.

🎯 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.