Senior ML Engineer - Sao Paulo

Rad Hires; São Paulo; Híbrido; BRL 543.000; Tempo integral

Resumo da oferta

A data-driven global organization seeks a Senior Machine Learning Engineer to design, scale, and optimize ML systems in a hybrid work setting. The ideal candidate has over 7 years of experience in ML engineering, advanced skills in Python and SQL, and expertise in MLOps practices. You will work on cutting-edge projects with complex data and influence key architectural decisions. This position offers a competitive salary and the opportunity to contribute to significant business outcomes.

Qualificações

• 7+ years in machine learning engineering, data engineering, or applied data science.
• Advanced Python and SQL skills with production-grade coding practices.
• Experience with cloud data platforms and MLOps workflows.

Responsabilidades

• Design and maintain scalable ETL/ELT pipelines.
• Build, train, validate, and deploy machine learning models.
• Implement MLOps best practices and ensure high reliability.

Conhecimentos

Machine Learning Engineering

Data Engineering

Python

SQL

MLOps

NLP

Model Development

Formação académica

Degree in Computer Science, Engineering, Math, or Statistics

Ferramentas

Databricks

Snowflake

BigQuery

scikit-learn

PyTorch

Transformers

Descrição da oferta de emprego

Senior Machine Learning Engineer

Base Salary: 100K USD annually
Work Model: Hybrid – 3 days per week on-site in the office in Santo Amaro, São Paulo - Brazil

About the Role

We’re looking for an experienced Machine Learning Engineer to design, build, and scale intelligent data products that support real-world applications. This position bridges data engineering, model development, and productization — transforming large datasets and unstructured information into production-ready systems.

About the Client

You’ll be joining a global organization that operates at the intersection of data, risk modeling, analytics, and financial advisory, supporting large enterprises in making high-impact strategic decisions. The team works with highly complex datasets and delivers advanced analytical and predictive solutions that directly influence business outcomes.

You’ll collaborate with engineering, product, and data teams to create end-to-end ML pipelines, optimize model performance, and drive experimentation with cutting‑edge NLP and LLM techniques. This role is ideal for someone who thrives on building robust, scalable ML infrastructure from the ground up.

What You’ll Do

• Design and maintain scalable ETL/ELT pipelines to support analytical and ML systems.
• Build, train, validate, and deploy machine learning models with reproducible and automated workflows.
• Implement MLOps best practices, including packaging, orchestration, CI/CD, and production monitoring.
• Manage data structures, feature stores, and metadata to ensure high reliability and accessibility.
• Develop data products and APIs to expose ML insights to internal systems and stakeholders.
• Continuously optimize system performance, cost efficiency, and scalability across cloud environments.
• Establish standards for observability: logging, alerts, data lineage, documentation.
• Collaborate with product and business teams to define success metrics and measurable impact.
• Mentor engineers and data scientists in ML system design and operations.

What You Bring

• 7+ years in machine learning engineering, data engineering, or applied data science.
• Degree in CS, Engineering, Math, Statistics, or equivalent experience.
• Advanced Python and SQL skills, with production‑grade coding practices.
• Strong knowledge of testing, data validation, and ML quality assurance.
• Experience with cloud data platforms (Databricks, Snowflake, BigQuery).
• Familiarity with dbt or similar transformation frameworks.
• Expertise with scikit‑learn, PyTorch, or Transformers for model development.
• Proven experience implementing MLOps workflows with CI/CD and containerization (K8s or serverless).
• Experience with NLP tools and techniques (spaCy, NLTK, prompt engineering).
• Proficiency integrating ML systems with REST APIs.
• Ability to design scalable and observable production ML systems.
• Experience mentoring or leading small technical teams.

Bonus Points

• Experience deploying ML models at scale with latency/cost optimization.
• Background in consulting, tech, or fast‑paced product environments.
• Familiarity with financial/risk modeling.
• Knowledge of BI tools (Looker, Tableau, Power BI).
• Experience with vector databases (e.g., LanceDB) and RAG pipelines.
• Experience using data labeling platforms like Label Studio.

Why Join

You’ll help build the ML and data foundation of a global analytics‑driven organization, influence architectural decisions, and work with cutting‑edge NLP and generative AI technologies — all within a high‑impact team focused on measurable business outcomes.