Machine Learning Engineer | Senior

Jobgether SRL; Brasil; On-site; BRL 300,000 - 420,000; Full time

Benefits offered by this job

Remote work within Brazil

Hands-on AWS SageMaker exposure

Work on VoC platform scaling

Exposure to Generative AI tools

Continuous learning opportunities

Collaborative, multidisciplinary team

Job summary

Lever, Inc. is seeking a Senior Machine Learning Engineer in Brazil to industrialize the core ML model for a Voice of the Customer platform.

You will transform notebook experiments into scalable, production‑ready pipelines on AWS, with emphasis on MLOps, governance, and statistical validation. The role blends hands‑on ML engineering with platform evolution and cross‑functional collaboration.

Qualifications

• Proven professional experience developing and deploying ML solutions using Python in production environments.
• Strong experience with PyTorch and hands-on development of machine learning models.
• Practical experience with AWS SageMaker, including Jobs, Pipelines, Model Registry, and GPU‑based workloads.
• Solid understanding of MLOps practices, including model and experiment versioning, reproducibility, deployment, and governance.
• Experience with statistical validation and model comparison, with the ability to assess model consistency and performance rigorously.
• Strong analytical and problem‑solving skills with a hands‑on approach to investigating technical challenges.
• Experience with PyTorch Geometric and Graph Neural Networks (GNNs) is a plus.
• Knowledge of NetworkX, scikit‑learn, and FAISS is considered an advantage.
• Experience with model explainability is a plus.

Responsibilities

• Industrialize the core ML model for a VoC platform, moving models from experimentation into production environments.
• Transform notebook-based ML solutions into scalable, production-ready pipelines on AWS.
• Structure and maintain the MLOps lifecycle with versioning, reproducibility, governance, and experiment management.
• Develop and manage training and inference pipelines using SageMaker, including Jobs, Pipelines, Model Registry, and GPU workloads.
• Perform statistical validation to ensure score parity and consistency between existing and production models.
• Contribute to evolving a VoC platform that integrates data from multiple channels to generate insights.
• Collaborate across technical initiatives to improve reliability, scalability, and operational maturity of ML solutions.

Skills

Python

PyTorch

AWS SageMaker

MLOps

Statistical validation

Analytical skills

GNNs

NetworkX

scikit-learn

FAISS

Model explainability

Tools

PyTorch Geometric

NetworkX

scikit-learn

FAISS

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer | Senior based in Brazil.

This role offers the opportunity to industrialize and scale the core machine learning model behind a Voice of the Customer (VoC) platform. You will transform experimental models developed in notebooks into robust, production-ready pipelines running on AWS. The position combines hands‑on machine learning engineering with MLOps, model governance, statistical validation, and platform evolution. You will work with technologies such as Python, PyTorch, and Amazon SageMaker to build scalable training and inference workflows. Your work will directly contribute to turning data from multiple customer channels into actionable business insights. This is a senior‑level opportunity in an AI‑driven environment focused on engineering excellence, scalability, and continuous innovation.

Accountabilities

• Industrialize the core machine learning model of a Voice of the Customer platform, moving models from experimentation into reliable production environments.

• Transform notebook‑based machine learning solutions into scalable, parametrized, and production‑ready pipelines on AWS.

• Structure and maintain the MLOps lifecycle, ensuring robust model versioning, reproducibility, governance, and experiment management.

• Develop and manage training and inference pipelines using Amazon SageMaker, including Jobs, Pipelines, Model Registry, and GPU‑based workloads.

• Perform statistical validation to ensure score parity and consistency between existing models and their production‑migrated versions.

• Contribute to the evolution of a VoC platform that integrates data from multiple channels to generate meaningful business insights.

• Collaborate across technical and analytical initiatives to improve the reliability, scalability, and operational maturity of machine learning solutions.

Requirements:

• Proven professional experience developing and deploying machine learning solutions using Python in production environments.

• Strong experience with PyTorch and hands‑on development of machine learning models.

• Practical experience with AWS SageMaker, including Jobs, Pipelines, Model Registry, and GPU‑based workloads.

• Solid understanding of MLOps practices, including model and experiment versioning, reproducibility, deployment, and governance.

• Experience with statistical validation and model comparison, with the ability to assess model consistency and performance rigorously.

• Strong analytical and problem‑solving skills, with a hands‑on approach to investigating technical challenges and improving ML systems.

• Experience with PyTorch Geometric and Graph Neural Networks (GNNs) is a plus.

• Knowledge of NetworkX, scikit‑learn, and FAISS is considered an advantage.

• Experience with machine learning model explainability is a plus.

• Ability to work collaboratively in a technically sophisticated, AI‑focused environment while maintaining strong ownership of deliverables.

Benefits:

• Remote work opportunity within Brazil.

• Opportunity to work on the industrialization and scaling of production machine learning models.

• Hands‑on exposure to AWS, SageMaker, PyTorch, MLOps, and modern AI engineering practices.

• Opportunity to contribute to a Voice of the Customer platform that transforms multi‑channel data into business insights.

• Environment focused on Artificial Intelligence, Generative AI, advanced technologies, and digital transformation.

• Access to AI‑driven tools and cutting‑edge technologies supporting the development of digital‑native platforms.

• Opportunities for continuous learning, knowledge sharing, and exposure to evolving technology trends.

• Strong potential for personal and professional growth in a technology‑driven environment.

• Opportunity to collaborate with talented, multidisciplinary professionals on impactful AI initiatives.