53 Github Copilot Jobs - Page 3

Primary Responsibilities: Responsible for enhancement and development, testing, and deployment of a Java backend multithreaded streaming application. Responsible for debugging and Prod Issue resolution related to Java applications. Responsible for Java and other library upgrades in the applications. Should be able to articulate various aspect of REST like usage of verbs, how to design resources, how data model should be, etc. Should be able to create and maintain UI components in Angular. Collaborate with product, experience, and integration teams across the enterprise to provide a detailed API specification. Help maintain and identify improvements to REST API standards. Creation of API Mocks. Responsible for maintaining standards and best practices for the implementation of REST APIs. Work with infrastructure engineers to ensure smooth deployments and sustainable performance using Azure Kubernetes Service Qualifications: 5-6+ Years of Java Development Experience. Exp on Java 8 & Java 17 Exp on Java, Spring Boot, Spring Framework, multithreading, logging. Angular for UI Exp on Kafka or Microsoft Eventhub. Exp on Azure NoSQL i.e. Cosmos DB. Must have excellent debugging and Production Issue resolution skills. Exp on Microsoft Azure Cloud, Identity Management, Azure Key Vault AKS, Eventhub, Cosmos, ADLS. Exp on CI/CD pipeline for building and deploying code artifacts. Exp with various flavors of API (API Life Cycle Management, API Gateway Architect, API Management & API Publishing, API Versioning, API SecurityAuthorization & Authentication) Github or other distributed versioning control. Exp on Unit test cases frameworks like Junit, Mokito, PowerMokito etc Must have experience in using GenAI tools like Github Copilot for faster development and productivity. Good to have Node.js , React, Python, C#. Please share your updated cv on Avani.Vibhute@rigvedtech.com

As a Data Architect, you are required to: Design & develop technical solutions which combine disparate information to create meaningful insights for business, using Big-data architectures Build and analyze large, structured and unstructured databases based on scalable cloud infrastructures Develop prototypes and proof of concepts using multiple data-sources and big-data technologies Process, manage, extract and cleanse data to apply Data Analytics in a meaningful way Design and develop scalable end-to-end data pipelines for batch and stream processing Regularly scan the Data Analytics landscape to stay up to date with latest technologies, techniques, tools and methods in this field Stay curious and enthusiastic about using related technologies to solve problems and enthuse others to see the benefit in business domain Qualification : Bachelor's or Master's in Computer Science & Engineering, or equivalent. Professional Degree in Data Engineering / Analytics is desirable. Experience level : Minimum 8 years in software development with at least 2 - 3 years hands-on experience in the area of Big-data / Data Engineering. Desired Knowledge & Experience: Data Engineer - Big Data Developer Spark: Spark 3.x, RDD/DataFrames/SQL, Batch/Structured Streaming Knowing Spark internals: Catalyst/Tungsten/Photon Databricks: Workflows, SQL Warehouses/Endpoints, DLT, Pipelines, Unity, Autoloader IDE: IntelliJ/Pycharm, Git, Azure Devops, Github Copilot Test: pytest, Great Expectations CI/CD Yaml Azure Pipelines, Continuous Delivery, Acceptance Testing Big Data Design: Lakehouse/Medallion Architecture, Parquet/Delta, Partitioning, Distribution, Data Skew, Compaction Languages: Python/Functional Programming (FP) SQL: TSQL/Spark SQL/HiveQL Storage: Data Lake and Big Data Storage Design Additionally it is helpful to know basics of: Data Pipelines: ADF/Synapse Pipelines/Oozie/Airflow Languages: Scala, Java NoSQL: Cosmos, Mongo, Cassandra Cubes: SSAS (ROLAP, HOLAP, MOLAP), AAS, Tabular Model SQL Server: TSQL, Stored Procedures Hadoop: HDInsight/MapReduce/HDFS/YARN/Oozie/Hive/HBase/Ambari/Ranger/Atlas/Kafka Data Catalog: Azure Purview, Apache Atlas, Informatica Big Data Architect Expert: in technologies, languages and methodologies mentioned in Data Engineer - Big Data Developer Mentor: mentors/educates Developers in technologies, languages and methodologies mentioned in Data Engineer - Big Data Developer Architecture Styles: Lakehouse, Lambda, Kappa, Delta, Data Lake, Data Mesh, Data Fabric, Data Warehouses (e.g. Data Vault) Application Architecture: Microservices, NoSql, Kubernetes, Cloud-native Experience: Many years of experience with all kinds of technology in the evolution of data platforms (Data Warehouse -> Hadoop -> Big Data -> Cloud -> Data Mesh) Certification: Architect certification (e.g. Siemens Certified Software Architect or iSAQB CPSA) Required Soft-skills & Other Capabilities: Excellent communication skills, in order to explain your work to people who don't understand the mechanics behind data analysis Great attention to detail and the ability to solve complex business problems Drive and the resilience to try new ideas, if the first ones don't work Good planning and organizational skills Collaborative approach to sharing ideas and finding solutions Ability to work independently and also in a global team environment.

About the Role Were looking for a passionate AI Evangelist & Code Generation Expert to lead the adoption of AI-powered coding tools across our engineering teams. In this pivotal role, you’ll drive a transformation in how we build software—spanning new development, code refactoring, test generation, tech stack and microservice migration. You'll champion tools like Cursor AI, GitHub Copilot, and Windsurf AI, deliver impactful training, and demonstrate measurable gains in productivity and code quality. Experience applying AI in our domain is a strong plus. Key Responsibilities Champion AI-Powered Coding : Advocate the adoption of tools like Cursor AI, GitHub Copilot, and Windsurf AI across engineering teams, articulating their value and addressing resistance. Drive Training & Enablement : Design tailored training sessions, workshops, and resources to onboard developers at all levels and promote effective tool usage. Lead Adoption & Strategy : Build strategies to integrate AI tools into dev workflows, track adoption, and identify improvement areas. Set Standards & Best Practices : Establish clear usage guidelines ensuring code quality, security, and maintainability. Promote High-Impact Use Cases : Apply AI to areas like app development, code refactoring, tech stack migration, test generation, and microservice transformation. Measure ROI : Define and track metrics like dev velocity and bug reduction to demonstrate impact and adoption success. Provide Ongoing Support : Be the go-to expert for AI coding tools, offering mentorship, troubleshooting, and continuous guidance. Stay Ahead of the Curve : Monitor emerging AI technologies and recommend tools aligned with org goals. Collaborate with Engineering Leaders : Align AI strategy with overall tech objectives in partnership with managers and architects. Domain-Specific AI Use Case Exploration : Explore domain-specific AI opportunities beyond coding, such as Gen AI, NLP, computer vision, or document classification. Qualification & Functional Skills Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related fields. 5+ years of experience in software development with strong knowledge of SDLC, architecture, and testing practices. At least 1 year of hands-on experience with AI-powered coding tools like Cursor AI, GitHub Copilot, or Windsurf AI. Excellent communication and presentation skills; able to evangelize complex concepts to both technical and non-technical audiences. Proven ability to design and deliver impactful technical training sessions and workshops. Strong analytical mindset with the ability to measure and communicate technology impact. Comfortable working independently and collaboratively in a fast-paced, evolving environment. Passionate about emerging technologies, especially in the AI space. Proficient in code refactoring, software design principles, and modern dev tools and frameworks. Preferred : Experience applying AI in domains like Gen AI, classification, computer vision, or NLP. Preferred : Familiarity with cloud platforms (AWS, Azure, GCP) and microservices architecture.