About the Role
As an AI/ML Platform Engineer at Cobrainer, you will design, build, and maintain scalable infrastructure to support our AI and machine learning operations. You will play a central role in integrating large language models and skill graphs into distributed AWS-based systems, ensuring reliability, scalability, and efficiency. Beyond infrastructure, you will contribute to data orchestration and model development, helping to future-proof Cobrainer's Skills AI solutions.
Responsibilities
- Break down product requirements into actionable engineering tasks for your team.
- Explain data constraints to engineering, product, and stakeholder teams.
- Integrate AI/ML models into distributed cloud architectures on AWS.
- Design and implement scalable infrastructure using AWS services (Fargate, Lambda, ECS, etc.).
- Develop and maintain robust data pipelines and orchestration processes.
- Enhance automated deployment, logging, and monitoring setups.
- Contribute to text analysis and NLP models to extract and structure core business data.
Qualifications
- University degree in Computer Science, Data Science, Statistics, or a related field.
- 4+ years of industry experience in building data-centric software frameworks, including infrastructure.
- Strong foundation in OOP software development.
- Proficiency in Linux-based software development.
- Experience with container technologies (Docker), version control (GitLab/GitHub), and CI/CD pipelines.
- Agile mindset with experience in modern development practices.
- Fluent in written and spoken English.
Required Skills
- Advanced fluency in modern Python development, including database management and software testing.
- Hands-on cloud-native development experience on AWS.
- Proven track record in large-scale data pipeline orchestration.
- Experience preparing and manipulating datasets for model evaluation (structured, semi-structured, and unstructured data).
Preferred Skills
- Practical experience with the latest large language model (LLM) developments.
- Familiarity with data science frameworks (NumPy, pandas, scikit-learn).
- Experience with deep learning frameworks (PyTorch, TensorFlow).
- Knowledge in one or more of: entity extraction/linking, document classification, knowledge graphs, recommendations/matching.
- Experience with orchestration and ML platforms such as Prefect, Airflow, Kubeflow, SageMaker.
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