Category: IT Services
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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.
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