[Job-31614] Senior Machine Learning Engineer, Brazil

At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.

With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.

We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.

About the Opportunity

We are looking for a Senior Machine Learning Engineer to lead the development, industrialization, and evolution of Machine Learning products at an enterprise scale.

This professional will work at the intersection of Data Science, Data Engineering, and MLOps , taking ownership of the architecture, governance, operationalization, and support of ML solutions in production. The role requires end-to-end ownership, from solution design and development to monitoring, documentation, and continuous improvement.

Key Responsibilities

Lead MLOps initiatives , including model training, deployment, model serving, monitoring, and lifecycle governance.

Develop and maintain ETL/ELT pipelines, DAGs, and data and Machine Learning workflows using PySpark.

Design and manage enterprise Feature Stores , ensuring feature versioning, lineage, and consistency between training and inference.

Develop, validate, and operationalize Machine Learning models for different analytical use cases.

Implement model versioning strategies, Champion/Challenger approaches, rollouts, model promotion, and Model Registry management .

Ensure observability, quality, traceability, reproducibility, and governance across data, features, pipelines, and models.

Design and implement CI/CD processes and Infrastructure as Code (IaC) for Machine Learning platforms.

Define architectural standards, engineering best practices, and MLOps guidelines.

Conduct technical code reviews, support Data Scientists in industrializing ML solutions, and maintain technical, architectural, and operational documentation.

Required Qualifications

Advanced experience with Databricks , including MLflow, Unity Catalog, Delta Lake, Databricks Workflows, Model Registry, Model Serving, and Databricks Asset Bundles (DABs) .

Strong experience developing, operationalizing, and monitoring Machine Learning models in production .

Experience with Feature Engineering, hyperparameter optimization, model evaluation, and supervised and unsupervised learning algorithms .

Experience with enterprise Feature Stores , including feature versioning and point-in-time lookups.

Knowledge of Data Drift, Concept Drift, Performance Drift , and observability of data and ML pipelines.

Experience building CI/CD pipelines , managing DEV, QA, and PROD environments, and implementing Infrastructure as Code.

Experience with automated testing for data and Machine Learning pipelines.

Experience with distributed processing and Spark workload optimization .

Strong proficiency in Python, PySpark, SQL, MLflow, Spark MLlib , and key Machine Learning ecosystem libraries.

Knowledge of secure credential and secrets management, such as Service Principals, Key Vault, or equivalent solutions .

Experience with Azure DevOps or equivalent tools .

Languages

Intermediate English .

Ability to interact with global teams and produce technical documentation in English.

Nice to Have

Databricks Certified Machine Learning Professional – highly desirable.

Databricks Certified Data Engineer Professional.

Experience with GenAI, LLMOps, and RAG architectures .

What We’re Looking For

We are looking for a highly technical, hands-on professional with a strong architectural mindset, capable of transforming analytical models into scalable, production-ready solutions.

Beyond developing models, this professional will be responsible for ensuring that Machine Learning solutions are governed, observable, auditable, reproducible, and sustainable throughout their lifecycle , while leading new initiatives and continuously evolving the organization's data and MLOps platform.