Senior Machine Learning Engineer
Summary
Remote senior machine learning engineer role (placed via Look4IT for a client) improving a globally deployed recommender system. Day to day: driving MLOps practices, building GitLab CI/CD pipelines, managing models with MLflow, deploying on AWS SageMaker, and building scalable Python ML/data pipelines while mentoring other engineers.
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