This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning | MLOps Engineer based in Brazil.
This is an exciting opportunity for an experienced MLOps professional to drive the operationalization of machine learning solutions in a highly innovative and AI-driven environment. In this role, you will be responsible for deploying, scaling, and monitoring production-grade ML systems while ensuring reliability, observability, and cost efficiency across cloud infrastructures. Working closely with data scientists, data engineers, and platform teams, you will help bridge the gap between model development and business impact. The position offers exposure to modern AI practices, cloud-native technologies, and large-scale machine learning ecosystems, making it ideal for professionals passionate about building robust and intelligent platforms.
Accountabilities:
- Deploy and maintain machine learning models in production environments, supporting both batch and real-time inference scenarios.
- Build, optimize, and maintain end-to-end CI/CD pipelines for machine learning workflows, from training and validation to deployment and monitoring.
- Manage and evolve Feature Store environments, including feature creation, versioning, and availability for training and inference.
- Monitor production models by tracking performance metrics such as data drift, model drift, latency, throughput, and overall system health.
- Implement model governance practices, including experiment tracking, model versioning, and registry management.
- Ensure robust observability through logging, monitoring tools, and alerting mechanisms for ML services.
- Optimize cloud infrastructure usage and costs, applying FinOps best practices across machine learning workloads.
- Collaborate closely with multidisciplinary teams to deliver scalable, secure, and business-oriented AI solutions.
- Contribute to deployment strategies, platform improvements, and the continuous evolution of MLOps capabilities.
Requirements:
- Strong hands-on experience with AWS services, particularly Amazon SageMaker, S3, IAM, VPC, CloudWatch, Lambda, and Step Functions.
- Proven experience deploying machine learning models using real-time endpoints, batch processing, serverless inference, and multi-model endpoints.
- Advanced Python skills for developing training scripts, inference services, and automation workflows.
- Solid understanding of CI/CD practices and tools such as GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline.
- Experience with Infrastructure as Code (Terraform, CloudFormation, or AWS CDK).
- Strong SQL skills and experience handling large-scale datasets and data pipelines.
- Knowledge of model performance monitoring, including metrics related to accuracy, drift detection, latency, and throughput.
- Experience with observability and monitoring tools in production environments.
- Strong communication and collaboration skills to work effectively with data science, engineering, and infrastructure teams.
Nice to have:
- Experience with SageMaker Studio, Pipelines, Feature Store, Model Registry, and Model Monitor.
- AWS certifications, such as Machine Learning Specialty or Solutions Architect.
- Experience with Kubernetes/EKS and containerized ML workloads.
- Familiarity with computer vision models and AI-first methodologies.
- Knowledge of regulated environments, data privacy standards, and LGPD compliance.
- Experience implementing advanced deployment strategies such as blue/green, canary, or shadow deployments.
- Exposure to observability platforms like DataDog or Grafana.
- Familiarity with generative AI agents, multi-step workflows, and Model Context Protocol (MCP) integrations.
Benefits:
- Comprehensive medical and dental insurance plans.
- Meal and food allowance.
- Childcare assistance.
- Extended parental leave policies.
- Profit Sharing Program (PLR).
- Life insurance coverage.
- Well-being and fitness partnerships through Wellhub (Gympass) and TotalPass.
- Continuous learning platform and access to professional development programs.
- Partnerships with online learning and language platforms.
- Employee discounts and wellness initiatives.
- Dedicated programs focused on physical, mental, and family well-being.
- Inclusive environment with specialized support for accessibility and diversity initiatives.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
#LI-CL1
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Your email job alert is now active!
