AI/ML Engineer | Talent Marketplace
The posting does not state a salary. This range is our estimate from the role, the location and the stack — treat it as a guide, not an offer.
- Level
- Mid
- Apply from
- Philippines, Serbia, Mexico, Argentina, Poland, Brazil
About the role
- Design and deploy machine learning models.
- Collaborate with data engineers and product teams.
- Monitor model performance post-deployment.
- Stay current with AI/ML research advancements.
- Contribute to MLOps practices and tooling.
Skills the posting asks for
- Machine Learning
- NLP
- Data Pipelines
Summarised from the employer’s posting, which is reproduced in full below.
The employer’s full posting
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AI/ML ENGINEER
Vertical: Tech
Location: Remote - Philippines, Eastern Europe, and Latin America
USD Salary: Negotiable based on experience
The AI/ML Engineer is responsible for designing, building, and deploying machine learning models and AI-powered systems that solve real business problems. This role spans the full ML lifecycle - from data preparation and model development to production deployment and monitoring - and requires a strong combination of software engineering discipline and data science expertise. The AI/ML Engineer collaborates closely with data engineers, product managers, and business stakeholders to deliver AI solutions that are accurate, reliable, and scalable.
KEY RESPONSIBILITIES
- Design, develop, and deploy machine learning models for classification, regression, NLP, computer vision, recommendation, or other applicable use cases.
- Work with data engineers to build and maintain data pipelines that feed ML model training and inference.
- Evaluate and select appropriate algorithms, frameworks, and architectures for each problem.
- Train, validate, and fine-tune models using best practices for avoiding overfitting and ensuring generalization.
- Deploy models to production environments and build robust inference pipelines.
- Monitor model performance post-deployment and implement strategies for model retraining and drift detection.
- Collaborate with product and engineering teams to integrate AI features into applications.
- Conduct experiments, document findings, and present insights to technical and non-technical stakeholders.
- Stay current with advances in AI/ML research and assess applicability to the business.
- Contribute to MLOps practices, tooling, and infrastructure.