AI Engineer
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Summary
Remote ML/AI Engineer who designs, builds, and deploys production AI systems, including custom models for domains like LLMs, NLP, and computer vision, plus the data and MLOps pipelines to train, serve, and monitor them. Core stack: Python, PyTorch/TensorFlow, Docker, Git, and cloud platforms such as AWS.
This is a remote position.
We are looking for a Machine Learning / AI Engineer to design, develop, and deploy AI systems that solve real-world problems at scale. The ideal candidate combines strong machine learning fundamentals with hands-on production experience, strong engineering skills, and the ability to work independently in an evolving environment.
Your Duties
- Design, develop, and deploy machine learning and AI systems for real-world applications.
- Build and optimize custom AI models for domain-specific tasks.
- Design and maintain data mining, data preprocessing, and data-labeling pipelines.
- Work with large datasets, including feature engineering and data preparation.
- Apply machine learning techniques across areas such as LLMs, NLP, computer vision, or other AI domains.
- Train, evaluate, optimize, and improve machine learning models.
- Develop and maintain model deployment and MLOps pipelines.
- Deploy and serve models using cloud platforms and containerized environments.
- Use tools such as Docker, Git, and relevant MLOps platforms in collaborative development workflows.
- Analyze model performance and identify opportunities for improvement.
- Communicate technical concepts and project progress to non-technical stakeholders.
Requirements
- 2–5 years of hands-on experience building and deploying ML/AI models in production environments.
- Strong proficiency in Python.
- Practical experience with PyTorch and/or TensorFlow.
- Experience designing and architecting custom AI models.
- Experience with LLMs, NLP, computer vision, or other AI domains.
- Strong understanding of supervised and unsupervised learning, deep learning architectures, optimization, and evaluation metrics.
- Experience with data preprocessing, feature engineering, data mining, and data-labeling pipelines.
- Familiarity with MLOps tools and model deployment platforms such as MLflow, Kubeflow, or SageMaker.
- Knowledge of cloud platforms and services used for model training and serving, such as AWS Lambda.
- Experience with Docker and containerized model deployment.
- Familiarity with Git and collaborative software development practices.
- Strong analytical and problem-solving skills.
- Ability to communicate complex technical concepts to non-technical stakeholders.
- Curiosity-driven mindset and comfort working with ambiguity.