ML Engineer (Python) - Remote (EMEA)
1 day ago⚠️ Before applying, please carefully review all the job requirements
- Location:
- Qatar, Dubai(UAE), Canada, Saudi Arabia, US, UK other Region
- Work Arrangement:
- Fully Remote
- Experience:
- Experience in ML Engineering
- Job Type:
- Contract
- Company:
- Proxify
- Salary:
- Negotiable
- Posted:
- 2026-10-06
Job Overview
- Proxify is seeking an experienced Machine Learning Engineer with advanced Python expertise to join its global network of remote software developers. In this full-time contract role, you will design, construct, deploy, and maintain end-to-end machine learning models and scalable data architectures that power intelligent features for international client applications.
- Operating primarily within European time zones, you will oversee the complete machine learning lifecycle. Responsibilities include evaluating new data streams, constructing predictive, classification, and recommendation algorithms, and writing production-grade code for cloud and on-premises infrastructure. You will also implement robust MLOps practices, including continuous integration and delivery (CI/CD) pipelines, model performance monitoring, and data pipeline management.
Key Responsibilities
- ▪ Construct, manage, and monitor reliable data processing pipelines and production machine learning models.
- ▪ Engineer and refine predictive, classification, and recommendation systems to solve complex business challenges.
- ▪ Evaluate and incorporate alternative data sources to boost model accuracy and predictive power.
- ▪ Partner with software engineers, analysts, and product teams to enhance overall application capabilities.
- ▪ Produce robust, production-grade Python code tailored for both cloud-based and on-premise environments.
- ▪ Advance MLOps practices by establishing automated CI/CD workflows, model tracking, and lifecycle management.
Required Skills and Qualifications
- ▪ Demonstrated background in engineering and deploying production-level machine learning solutions.
- ▪ Advanced proficiency in Python across all stages of the machine learning development lifecycle (experience in R or Java is advantageous).
- ▪ Practical expertise working with core ML frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.
- ▪ Familiarity with modern MLOps tooling (including MLflow, Kubeflow, or Airflow) and major cloud platforms (AWS, GCP, or Azure).
- ▪ Strong foundation in statistical analysis, data mining methodologies, and quantitative data evaluation.
- ▪ Hands-on experience working with relational databases, data warehousing solutions, and massive datasets.
What the Company Offers
- ▪ Reliable, on-time monthly compensation with flexible withdrawal options.
- ▪ Consistent 8-hour daily client project schedules for predictable work-life balance.
- ▪ Up to 24 paid flex days off per year for full-time contracted positions.
- ▪ Access to long-term remote engineering engagements with international companies.
- ▪ Personalized candidate matching without repetitive technical assessments for future projects.
- ▪ Streamlined, one-time contracting process granting access to multiple client opportunities.
- ▪ Guaranteed consistent monthly pay structure for placed positions.
Application Tips
- Applicants may want to highlight their experience managing production machine learning systems throughout the full software development lifecycle. Emphasizing hands-on projects involving model deployment, real-time monitoring, and MLOps tooling—such as Kubeflow, MLflow, or Airflow—will showcase practical readiness. In your resume, present concrete examples of training predictive or recommendation models using PyTorch, TensorFlow, or Scikit-learn alongside Python data engineering pipelines.
Prepare for Your Interview
- These suggested questions can help you prepare for an interview by focusing on the skills, responsibilities, and technical areas relevant to this role.
Suggested Interview Questions
- Q: How do you structure production-ready Python code when building scalable data pipelines and deploying machine learning models?
- Q: Can you explain your approach to managing model versioning, tracking, and monitoring in production using MLOps tools like MLflow or Kubeflow?
- Q: How do you handle high-dimensional or noisy datasets when training classification and recommendation models?
- Q: What techniques do you use to evaluate and optimize model performance, accuracy, and latency for real-world business applications?
- Q: How do you implement CI/CD practices specifically tailored for machine learning workflows and automated retraining pipelines?
- Q: Can you describe your experience integrating third-party data sources or data warehouses into existing ML pipelines?
- Q: How do you approach choosing between frameworks like PyTorch, TensorFlow, and Scikit-learn for a given machine learning problem?
- Q: How do you collaborate with software engineers and product managers to ensure machine learning models integrate seamlessly into client software systems?
Interested Candidates Fill the Application Job Form: Apply Here
