[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.