Genes Developer

General

Youpal Group

Sweden - Remote

June 13, 2024

Deadline

Work Level

Employee Type

Project and Team

Our client is looking for multiple experts in artificial intelligence to join a generative AI project team. The expert will be responsible for developing AI solutions including various generation and analysis tools, which will be used in the client's web services and applications.

The tasks will include, but not limited to, development of generation and analysis tools, development of recommendation models, testing and improvement of generative tools, and creating suitable solution architectures. The development will primarily take place on the Google Cloud Platform.

This is a bidding project. The work will be conducted in a hybrid manner, requiring presence at the office 1-3 days per week.

Expectations from the expert:

At least 3 years of experience in machine learning with Python (including TensorFlow, Numpy, Pandas).
At least 3 years of experience in developing and maintaining production environments for machine learning algorithms.
At least 3 years of experience in developing and maintaining cloud services (GCP, AWS or Azure) for production use.
At least 5 years of experience in using SQL.
At least 3 years of experience in Infrastructure as Code (IaC) ETL and data pipeline solutions using Python.
At least 3 years of experience in A/B testing.

Other evaluated areas of expertise:

At least 6 months of experience in production use/development in one or more of the following technologies: Apache Airflow, Google Cloud Platform, Redis, Kubernetes, Terraform (each technology will be evaluated separately).
At least 6 months of experience in implementing REST/GraphQL interfaces for front-end applications.
Understanding of generative AI tools and their use cases: knowledge of model architectures (Encoder-decoder, Transformer).
Understanding of generative AI tools and their use cases: vector search algorithms (Tree-AH, ANN, ScaNN, KNN).
Understanding of generative AI tools and their use cases: ways to assess the effectiveness of RAG (