Offer summary

Qualifications:

Strong programming skills in Python or R, with experience in ML frameworks like TensorFlow and PyTorch., Experience with deploying and maintaining ML models using Docker and cloud-based AI services., Solid understanding of MLOps principles, including CI/CD for ML models and model monitoring., Strong analytical and problem-solving skills with attention to detail..

Key responsabilities:

Design, develop, and maintain ML models to address business challenges.

Implement and optimize ML algorithms for efficiency and scalability.

Monitor and troubleshoot the performance and accuracy of ML models in production.

Automate and streamline ML pipelines for smooth transitions from development to production.

Job description

About the Role

You will be responsible for optimizing end-to-end ML pipelines, automating workflows, managing model lifecycle operations (MLOps), and ensuring AI systems are scalable and cost-efficient. You will embrace a build-measure-learn approach, continuously iterating and improving models for performance and reliability.

Responsibilities

Design, develop, and maintain ML models to solve business challenges and drive automation.
Implement and optimize ML algorithms for efficiency, scalability, and AI-powered insights.
Conduct experiments, A/B testing, and model evaluations to improve performance.
Develop, containerize, and deploy AI/ML systems in production environments using best practices.
Automate and streamline ML pipelines, ensuring smooth transitions from development to production.
Monitor and troubleshoot the performance, accuracy, and drift of ML models in production.
Execute and automate model validation tests, ensuring robustness and reliability.
Optimize training and inference workflows, enhancing model efficiency and speed.
Manage model versioning, deployment strategies, and rollback mechanisms.
Implement and maintain CI/CD pipelines for ML models, ensuring smooth integration with engineering workflows.
Review code changes, pull requests, and pipeline configurations to uphold quality standards.
Stay updated with emerging AI/ML technologies, MLOps best practices, and cloud-based ML platforms.

Skills and Qualifications

Strong programming skills in Python or R, with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Experience deploying and maintaining ML models using Docker, Kubernetes, and cloud-based AI services (AWS Sagemaker, GCP Vertex AI, Azure ML).
Solid understanding of MLOps principles, including CI/CD for ML models, model monitoring, and automated retraining.
Knowledge of data engineering principles, data preprocessing, and feature engineering for ML pipelines.
Familiarity with workflow orchestration tools.
Experience with real-time model serving and API deployment.
Strong analytical and problem-solving skills with a keen attention to detail.
Ability to collaborate cross-functionally and work in a fast-paced AI-driven environment.

Required profile