3+ years of experience in software engineering with Scala/Java;
Experience with Streaming technologies (Kafka, Flink, Spark Streaming, Kinesis) is a must;
Strong knowledge in Functional Programming (Scala, Haskell, etc.);
Understanding of Event-Driven Architecture (Kafka, AWS Lambda);
Ability to understand and develop low-latency, high-performance backend applications;
Experience working with CI/CD environments, understanding of Docker concepts;
Upper-intermediate level of written and spoken English, ability to articulate and communicate clearly complex topics.
Experience with Kubernetes and Helm;
Experience with Protobuf serialisation;
Knowledge of Geometric Algorithms.
Higher Education: Bachelor’s Degree in Computer Science, Computer Engineering or Geoinformatics.
Besides such basics as a competitive salary, comfortable and motivating work environment, here at Intellias we offer:
For your professional growth —
Innovative projects with advanced technologies;
Individual approach to professional and career growth (Personal Development Plan);
Regular educational events with leading industry experts;
For your comfort —
Flexible working hours;
Spacious office with lots of meeting rooms;
Kids’ room with professional baby-sitter (offices in Lviv & Kyiv).
For your health —
3 health packages to choose from — medical insurance, sports attendance or mix of both;
Annual vitaminization program;
Annual vaccination and ophthalmologist check-up.
For your leisure —
Corporate celebrations and fun activities;
Beauty parlor (offices in Lviv & Kyiv).
Work closely with product owners and principal engineers to implement, test and continually improve scalable Java applications and services running on Kubernetes;
Take accountability for implementation and quality of software products as well as time and complexity estimation of own technical tasks;
Develop software products using Agile methods, tools, and continuous delivery process;
Participate in design reviews, code reviews, and product demos;
Collaborate with other engineering teams to resolve dependencies and deliver high-quality software on time.
Our client is a company that enables people, enterprises, and cities to harness the power of location. By making sense of the world through the lens of location, it empowers their customers to achieve better outcomes from helping a city manage its infrastructure or an enterprise optimize its assets to delivering drivers to their destination safely.
We are working on the Platform which provides the next generation of location based services intelligence. With every connected IoT device or sensor capable of generating and sharing location data, the Platform helps to make better use of that data and transform it into useful services for people and organizations all in real-time. The Platform is meant to become the go-to destination for location services, supporting not only autonomous vehicles but smart cities and intelligent transportation systems too.
Why we rock?
Data-centric development. We build reusable components that run complex data pipelines at scale through data management, processing and distribution services and APIs;
Visualized location intelligence. The maps rendering service we are working on is one of the key Platform’s client-facing features which helps businesses to make sense of location data by empowering 2D and 3D rendering capabilities of modern web browsers;
The way of working. Fresh setup, minimum to none legacy processes and technologies, a good chance to start over with a clean slate;
Best practices. Platform possesses strong background in continuous delivery approaches, automated testing, and employs the best DevOps practices to ensure the Platforms reliability at scale;
Self-fulfillment. Stand at the roots of the Platform that will redefine how society thinks about location data and boost your professional value by mastering edge data management techniques.
The Engineer we are looking for will be tasked with developing one of the mission-critical components responsible for the detection, extraction and processing of different road objects using ML algorithms in the received upstream messages coming from camera, radar, and lidar sources. It employs performant stream data processing and storage tools (Flink, Spark Streaming, Kafka) and scale across Amazon Web Services (k8s cluster).