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Bizico — це аутсорсингова компанія, ми розробляємо web та мобільні додатки. Основний стек технологій web: React, Node.js, .Net, та мобільна розробка: іOS, Android. Забезпечуємо повний цикл розробки проектів, від залучення бізнес-аналітика до кінцевого супроводу проекту. Основними клієнтами є замовники з США та Німеччини.
18 сентября 2019

Senior (Lead) Clojure Developer (вакансия неактивна)

Черновцы, удаленно

Необходимые навыки

REQUIREMENTS:
• 3+ years strong development experience with Java, Scala, Python, Lisp or ideally Clojure as a core tool
• Strong understanding of Functional Languages principles, OOP, SQL and databases
• Good knowledge of JavaScript, understanding of React or Angular
• Experience in building native mobile applications, understanding of React Native
• At least basic experience with Clojure/ClojureScript (couple months or more), willingness to advance with Clojure and ClojureScript
• Bachelor or master degree in computer science direction
• Advanced skills in software engineering, algorithms, and math
• English — intermediate+
• Strong understanding of software development life cycle
• Good communication skills

Предлагаем

WE OFFER:
• Interesting and diverse project from our American partner
• The best software and other necessary equipment
• Foreign language courses (Native Speaker)
• Financial support of your professional training and conferences
• Spacious and comfortable office
• Flexible working hours
• Paid vacation, paid sick leave
• Career and professional development

Обязанности

RESPONSIBILITIES:
• Work as a part of our product development team
• Participate in solution design and development, deliver high-quality code
• Regularly communicate with the team members in Ukraine and at the client-side, participate in status meetings, design sessions, and brainstorming
• Provide estimation and reporting for assigned tasks

О проекте

Project Description
Digital healthcare solution
Project Description. The purpose of the system is to use video interactions to improve patient care and automate diagnosis and detection of disease and predict treatment success/failure early, improve payment rates between patients, employers and healthcare providers. The primary goal of the initial stage is capturing a lot of videos to build out dataset to apply machine learning towards. The application consists of the mobile (iOS and Android) and web (primarily for back-office purposes) parts. The video/audio/language analysis with in-app analytics, notifications via SMS and push notifications to patients are essential parts of the system. It must be HIPPAA compliant and requires high levels of security, VPC, IDS, log management, OSSEC, DDoS protection via AWS Shield, etc. The system is in the MVP stage with a set of advanced functionalities still to be implemented. The software development team is doing the development of all major components of the system including AI/ML piece from the initial stages of the application.
ML/AI Outlines (MVP stage). The mobile app captures patients’ videos and sends them to the AWS S3 bucket. The videos are re-encoded and separated from audio with FFmpeg. The patient photo if not available is extracted from the video with the OpenCV based face detection tool. The audio files are processed with AWS Transcribe to extract meaningful text and word intervals. The word intervals are used for video player navigation by selecting a word. The text is processed with Python’s NLTK library to get an emotional coloring of the text and to build emotional estimations. The web application estimates the absolutist language score of the text for predicting Anxiety, Depression and Suicidal Ideation. The videos, texts, automatic scores, and estimations are available to the patient’s doctor helping them understand better a patient’s condition.
Web Front-end: React, ClojureScript, Reagent/Re-frame, Figwheel.
Mobile Front-end: React Native, ClojureScript, Reagent/Re-frame, Shadow-cljs.
Back-end: AWS (ECS) hosting. Server — Clojure (Luminus framework). AWS Lambdas — Node.js. Database — Datomic Cloud.
ML: Python-based libraries (NLTK library for sentiment analysis and others), AWS Transcribe (audio to text), OpenCV (face detect via haar cascades).
Additional technologies: AWS S3, AWS Cloudfront, FFmpeg (video re-encoding), dash/hls video streaming,
Deployment: Docker, AWS CodeBuild

Горячие вакансии

Все вакансии