Job Description

Our mandate is to quickly explore new technologies to determine what is relevant for our clients and firm to invest in. Our work is having a tremendous impact on how and our clients do business, whether we are streamlining workflows with machine learning models or helping clients make the right strategic investments in AI. Preferred Fields of Study : Analytics, Artificial Intelligence and Robotics, Business Analytics, Computer and Information Science, Management Information Systems, Mathematics, Computer E ngineering Knowledge Required : Demonstrates a thorough level of abilities with, and/or a proven record of success as both an individual contributor and team member addressing business client needs by: Researching, designing, developing, and testing blockchain solutions; Maintaining and extending current applications; and, Creating and maintaining documentation for the environment and solutions. Experience conducting research in a lab and publishing work is a plus. Experience with following technologies: Demonstrates extensive abilities and/or a proven record of success as a team leader in the following areas: Proven software development experience; Expertise in one or more blockchain platforms (Hyperledger Fabric, Corda, Etherium); Proven knowledge of blockchain protocols and various tradeoffs in terms of performance; Experience working with databases (PostgreSQL, CouchDB); Proficiency in one of the following languages: Java, NodeJS, Golang, Kotlin; Proficiency in change management tools such as Github; Experience working with Devops tools (Azure Devops,Jenkins) and ability to manage cloud environments including scaling and management of VMs; and, Knowledge of JSON-RPC Role and Responsibilities: Blockchain Developer will be responsible for designing, implementing, and supporting distributed ledger networks and solutions. Quickly explore new and existing technologies and evaluate their technical and commercial viability. Work in sprint cycles to develop proof-of-concepts and prototype models that can be demoed and explained to data scientists, internal stakeholders, and clients. Quickly test and reject hypotheses around data processing and model building. Experiment, fail quickly, and recognize when you need assistance vs. when you conclude that a technology is not suitable for the task.

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