Position: Neural Network Developer
Company: First Bridge
Experience required: 2y
Category: Data Science
English level: pre
Published: 2022-10-01T00:00:00+03:00

First Bridge company is looking for an experienced Neural Network Developer

**Part-Time**
**Spoken Ukrainian or russian**

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**Responsibilities:**
🔹Own, design, and develop Wand AI ML pipelines: provide efficient models training, testing, deployment, and monitoring;
🔹Apply scientific knowledge to analyze and collect data, identify problems, devise solutions and construct methodologies;
🔹Develop machine learning and deep learning models for tabular data (later: time series, NLP, CV, etc.);
🔹Learn and implement cutting-edge ML algorithms;
🔹Create and automate data pipelines including data extraction, validation & anomaly detection, indexing, and other data-related tasks;
🔹Follow the project from ideation to implementation;
🔹 Contribute to a wide variety of projects using a range of technologies and tools.

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**Requirements:**
🔸2+ years of industrial experience with ML, including training, testing, deployment, and monitoring real-time (or near real-time) machine learning models in production;
🔸Experience with multiple core systems, such as graphics, tools, audio, networking, memory handling, scripting, etc.;
🔸Proven experience with modern DL techniques;
🔸Experience with ML “understanding” techniques: explainability, confidence, drift, etc.;
🔸Deep knowledge of math, probability, linear algebra, computer vision and algorithms;
🔸Сoding skills - Python/Java/C/C++ , etc.;
🔸Proficiency with ML libraries (Tensorflow, Pytorch, LightGBM, Scikit-learn);
🔸Experience in Microservices architecture, cloud technologies, Docker, and Kubernetes.

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**Advantages:**
🔹Experience with data pipelines / big data.
🔹Experience designing and implementing AutoML flows/frameworks.

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**About the project**
This is a project in the field of marketing AI (artificial intelligence) tools, neural network technology, which is used in marketing processes, most often it is an analysis of audience feedback in relation to the product.