You will be responsible for enhancing the architecture, deployment processes, and operational backbone of the ML platform with a focus on MLOps and production-ready systems. Additionally, you will collaborate with cross-functional teams to ensure ML models are scalable, reliable, and reproducible.
This is a remote position.
We are looking for an experienced Senior Machine Learning Engineer to join our client and help further develop a successful, globally deployed recommender system. In this role, you will be responsible for enhancing the architecture, deployment processes, and operational backbone of the ML platform, with a strong focus on MLOps, AWS, and production-ready machine learning systems.
You will work closely with data scientists, engineers, product managers, and business stakeholders, providing technical guidance and helping ensure that ML models are scalable, reliable, reproducible, and production-ready.
Responsibilities:
Driving and improving MLOps practices across the ML environment
Building and optimizing CI/CD pipelines using GitLab
Implementing ML experiment tracking and model management with MLflow
Productionizing and deploying machine learning models using AWS SageMaker
Designing and maintaining scalable ML and data pipelines
Developing and maintaining Python-based ML and data infrastructure
Implementing monitoring and observability for ML systems
Providing technical guidance and mentoring to Data Scientists, Data Engineers, and MLOps Engineers
Applying software engineering best practices, including testing, documentation, and system design
Collaborating with Product Managers, Data Scientists, Engineers, and business stakeholders
Evaluating and introducing new technologies to improve ML capabilities
Requirements
5+ years of professional experience in Machine Learning Engineering
Strong experience deploying and maintaining production ML systems
Expert-level Python skills and knowledge of the data science ecosystem
Hands-on experience with AWS, preferably AWS SageMaker
Strong knowledge of MLOps practices and lifecycle
Practical experience with MLflow
Experience with GitLab CI/CD
Experience with at least one major deep learning framework, e.g. PyTorch or TensorFlow
Experience designing and building scalable ML and data pipelines
Experience with ML system monitoring and observability
Ability to design, document, and communicate complex technical architectures
Experience mentoring and providing technical guidance to other engineers and data scientists
Strong communication and stakeholder management skills
Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
Preferred:
Master's or PhD in Computer Science, AI, or Machine Learning
Experience with Prometheus, Grafana, or Evidently AI
Experience working with large-scale recommender systems
Strong understanding of software engineering and system design principles
Benefits
B2B contract
Engagement in technically challenging projects with a mature engineering culture
Friendly and collaborative work environment
Opportunities to work with modern technologies and enterprise-scale infrastructure
Supportive team culture focused on knowledge sharing and professional growth
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