Full stack Engineer

As a Full Stack Engineer, you will work within the development team to build the annotation platform on both sides front-end and back-end.
Collaborating with Product Managers, Architects and Software Engineers to research, design, implement and improve functionalities of the annotation platform as part of a Big Data analytic system.
  Key responsibilities Design, develop, test, refactor and scale the next-gen data analytics platform, that provides data management, ETL pipelines, ML models integrations, rich visualizations, and information management around that.
Be proactive in prototyping new ideas/components; Turn existing POC-level components into production-grade.
Participate in code reviews and thoughtfully provide constructive feedback.
Taking ownership of the whole vertical of certain functional area.
Increase and maintain test coverage.
Participate in overall architecture discussion meetings.
Provide clean trail of activity – source control, issue tracking, documentation.
·        Comply with QHSE (Quality Health Safety and Environment), Business Continuity, Information Security, Privacy, Risk, Compliance Management and Governance of Organizations policies, procedures, plans and related risk assessments.
Competitive Salary Class A Medical Insurance Requirements Computer science background, excellent knowledge of common data structures, and algorithms.
7+ years of experience building backend (java) and SPA web applications (react).
Knowledge of web fundamentals: protocols, modern web standards.
Pristine knowledge of modern React (composition patterns, hooks, data access), complex state management (Redux/Mobx).
Experience wrapping existing JS libraries (SKDs) with framework abstractions.
Solid web foundation knowledge (HTML, DOM CSS), experience with Tailwind is a plus Strong knowledge of Java platform (JSCP and/or Oracle certified professional Java SE11 will be a huge plus).
Strong experience working with core Spring components (Security, Data/JPA, Web) and low level JDBC.
Experience building durable long-running processing pipelines, task orchestration, scheduling, etc.
. Experience and passion in application profiling and low-level optimizations Fluent SQL is a must.
Ideally, it would be preferred if you also have Big data experience (Spark, Flink, Kafka, S3) Experience using, integrating etc… AI/ML models, knowledge of Python and common ML libs.
Experience building durable processing pipelines with Temporal.
io Experience working with column oriented OLAP engines (Clickhouse) Experience integrating ML models into the custom data pipelines.
Experience using Docker/Kubernetes/MLO