Full Stack Engineer

Project Role : Full Stack Engineer Project Role Description : Responsible for developing and/or engineering the end-to-end features of a system, from user experience to backend code. Use development skills to deliver innovative solutions that help our clients improve the services they provide. Leverage new technologies that can be applied to solve challenging business problems with a cloud first and agile mindset. Must have skills : AI Agents & Workflow Integration Good to have skills : React Native, Automation in Application Development, Microsoft Front End Development (FED) NextJS Minimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Summary: As a Specialist is a senior full-stack engineer and architect who improves how teams plan, build, test, review, deploy, monitor, and support software using AI. The core mandate is to help move the organization from Implemented AI toward Future Ready AI: turning individual AI uses into repeatable, measured, secure, scalable, and continuously improving engineering practices. This is a hands-on delivery role, not a strategy-only role. Roles & Responsibilities: - Expected to be an SME, collaborate and manage the team to perform. - Responsible for team decisions. - Engage with multiple teams and contribute on key decisions. - Provide solutions to problems for their immediate team and across multiple teams. - Lead the integration of cross-functional team efforts to ensure seamless delivery of project objectives. - Mentor junior team members to foster skill development and knowledge sharing. - Coordinate with stakeholders to align technical solutions with business goals. Professional & Technical Skills: - Must To Have Skills: Proficiency in AI Agents & Workflow Integration. - Good To Have Skills: Experience with React Native, Automation in Application Development, Microsoft Front End Development (FED) NextJS. - Full-stack engineering: Strong hands-on experience with TypeScript, JavaScript, React, Next.js, Node.js, Python, APIs, databases, cloud services, and modern web application architecture. - AI-enabled SDLC and maturity advancement: Evidence of using AI to improve requirements analysis, coding, code review, test generation, regression testing, defect triage, documentation, release readiness, or production support. Must know how to turn individual AI uses into repeatable, measured, team-wide practices that can scale safely. - Production architecture and integration: Ability to design AI-enabled workflows with clear data flows, secure APIs, permissions, integration boundaries, scalability, failure handling, and support ownership. - Quality, security, and operations: Experience with AI evaluation and testing, output validation, human approval for important actions, access controls, logging, monitoring, incident response, rollback, and production support.