28 days ago
Remote, APACMid Level / Senior
Responsibilities
- Design, develop, train, validate, fine-tune, and deploy machine learning models for classification, regression, NLP, computer vision, recommendation, and other use cases.
- Build and maintain data pipelines for ML training and inference in collaboration with data engineers.
- Select algorithms, frameworks, and architectures appropriate to each problem.
- Develop production inference pipelines and integrate AI features into applications.
- Monitor model performance, detect drift, and implement model retraining strategies.
- Conduct experiments, document findings, present insights, and assess advances in AI/ML research.
- Contribute to MLOps practices, tooling, and infrastructure.
Requirements
- 3–5 years of experience in machine learning engineering, data science, or a related field.
- Proficiency in Python and core ML libraries such as scikit-learn, TensorFlow, or PyTorch.
- Strong understanding of supervised and unsupervised learning, model evaluation, and feature engineering.
- Experience deploying ML models to production through APIs, batch pipelines, or embedded systems.
- Familiarity with Pandas, NumPy, and SQL for data manipulation and analysis.
- Knowledge of software engineering practices including version control, testing, and code quality.
- Strong analytical and problem-solving skills.
- Preferred qualifications include experience with LLMs, prompt engineering, generative AI, MLOps platforms, cloud ML infrastructure, data engineering pipelines, NLP techniques, or an advanced degree in Computer Science, Statistics, or a related field.
Benefits
- Remote work from the Philippines, Eastern Europe, or Latin America.
- Full-time and part-time remote opportunities may be available through the talent marketplace.
- Opportunity to become a HireLago Certified Professional through the vetting process.
- Profile showcase and access to exclusive remote opportunities with global employers.
- Matching based on skills, experience, schedule, and salary expectations.
- The service is free for professionals with no placement fees, subscriptions, or hidden costs.
Tech Stack
Apache AirflowApache SparkAWSAzuredbtGoogle Cloud PlatformMLflowNumPyPandasPythonPyTorchscikit-learnSQLTensorFlow
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