Description:

The MLOps Engineer is responsible for designing, implementing, and maintaining scalable, reproducible, and auditable machine learning pipelines.
This role bridges the gap between data science, engineering, and operations by enabling reliable deployment, monitoring, and governance of machine learning models in production environments.
Responsibilities include developing and maintaining end-to-end ML pipelines using Kedro, following best practices in modularity, version control, and data lineage.
The engineer will manage experiment tracking and model versioning with MLflow, deploy models using ArgoCD for GitOps workflows, and utilize Kubernetes (EKS) for container orchestration and scalability.
Setting up and managing observability and performance monitoring using the Grafana + Prometheus stack is also part of the role.
The engineer will handle artifact and dependency management using JFrog Artifactory and operate collaborative computing environments with JupyterHub for multi-user scenarios.
Automating the full ML lifecycle using CI/CD pipelines integrated with AWS (EKS, ECR, S3, Lambda, CloudWatch) is a key responsibility.

Requirements:

Candidates must have experience with pipeline orchestration tools such as Kedro and Argo Workflows, and ArgoCD.
Proficiency in experiment tracking using MLflow is required.
Knowledge of containerization and deployment using Docker, Kubernetes (EKS), AWS ECR, and Helm Charts is essential.
Familiarity with monitoring and observability tools like Grafana, Prometheus, and AWS CloudWatch is necessary.
Experience with infrastructure and DevOps tools, particularly AWS and Terraform (preferred), and JFrog Artifactory is required.
Candidates should have experience operating collaborative environments using JupyterHub, Git, and GitHub Actions / GitLab CI.
Knowledge of data versioning tools such as DVC and Delta Lake (preferred) is important.
A strong understanding of version control for code, data, and models is required.
Knowledge of security, authentication, and governance in data platforms is necessary.
Practical experience with automated deployment of ML models in real-time and batch settings is essential.
Familiarity with ML-specific CI/CD, including testing and model drift validation, is required.
Excellent communication skills and the ability to interface between data and engineering teams are necessary.
A degree in Computer Engineering, Data Science, Information Systems, or related fields is required.
Proven experience deploying and maintaining ML solutions in production environments is essential.

Health and dental insurance are provided to employees.
Meal and food allowances are included as part of the benefits package.
Childcare assistance is available for employees with children.
Extended paternity leave is offered to new fathers.
Access to Wellhub (Gympass) and TotalPass for fitness and wellness.
Profit-sharing (PLR) opportunities are available.
Life insurance is included in the benefits.
Employees can take advantage of CI&T University for professional development.
A discount club is available for various services and products.
A free online platform dedicated to physical, mental, and overall well-being is provided.
Courses on pregnancy and responsible parenting are offered.
Partnerships with online learning platforms for continuous education are available.
A language learning platform is included in the benefits.
More details about the benefits can be found on the company's careers page.