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Junior ML Engineer

Mad Devs
Компания Mad Devs
Тип Офис / Бишкек
Оклад 1200 - 1500 USD в месяц
Описание вакансии

We are currently engaged in a B2B project in which we are actively developing a product with a broad user base. This project is headquartered in Washington, DC. We have the privilege of working closely with a team of highly skilled professionals, including designers, sales managers, and business analysts.


Required Skills:


- Minimum 1 year of commercial experience with NLP and LLMs

- Familiar with technologies: Python, SQL, Pandas, Django, Pydantic, SQLAlchemy, Sklearn, PyTorch, Transformers, Git, BitBucket, CI / CD, Docker, OpenAI API, Airflow, BigQuery

- Knowledge of basic machine learning algorithms and metrics

- Experience implementing machine learning models in a production environment

- Familiarity with software development and delivery processes. Understand the key steps involved

- Can generate clear and achievable goals for machine learning tasks/projects

- Experience working with big data and systems to process it

- Know how to use data analysis to identify insights and patterns that can help solve business problems

- Knowledge of basic machine learning algorithms and metrics

- Experience implementing machine learning models in a production environment

- B2 English level, fluent Russian


Responsibilities:


- Develop and implement machine learning algorithms

- Transfer machine learning models from development to real-world application

- Compare different approaches to solve complex business problems

- Collaborate with customers to identify their problems and opportunities for solving them using machine learning tools

- Develop algorithms for recommendation and ranking systems

- Develop intelligent assistants

- Keep models and instructions for language models up to date


It will be a plus:


- Experience in applying hyperparametric optimization techniques to improve model performance

- Ability to monitor models in production

- Experience in the full ML model delivery cycle: from data collection to delivery to production and continuing support; understanding of the technical background of deliveries