Custom Software Engineer

Project Role : Custom Software Engineer Project Role Description : Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs. Must have skills : Enterprise Architecture Framework Good to have skills : NA Minimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education

Summary: We are accelerating growth and value for our clients by combining creativity, technology, and data-driven intelligence. Within our Commerce and AI practice helps organizations design, build, and scale modern digital commerce ecosystems - embedding Generative AI and Agentic AI into commerce to enable real-time personalization, autonomous merchandising, and intelligent customer engagement at scale. About the role. We are entering an exciting era of agentic commerce where AI agents, conversational interfaces, and intelligent systems are reshaping how people discover, evaluate, and buy online. We are looking for technically strong architects who can help design and deliver the systems that make this real - working at the intersection of enterprise commerce platforms and emerging agentic AI technologies.

Roles & Responsibilities: Own the architecture and drive its implementation not just designing agentic commerce solutions, but ensuring they are built correctly, consistently, and to the standard defined. Accountable for what gets delivered, not just what gets designed. Translate architecture into working systems taking multi-agent patterns, RAG pipelines, LLM-powered applications, and orchestration designs through to production, ensuring engineering teams build what was architected and course-correcting when they don't. Be the technical authority across the full delivery lifecycle from integration design with enterprise platforms (OMS, PIM, CRM, payments, fulfilment) through to verifying that APIs, event-driven patterns, and agentic protocols are implemented correctly in code and configuration. Ensure the data and knowledge foundations are built right hands-on oversight of embedding pipelines, vector retrieval, semantic search, and product data structures, validating that what underpins agent intelligence is production-grade and commercially reliable. Hold delivery teams to architectural standards — reviewing code, validating technical decisions, and maintaining coherence across workstreams so that implementation drift doesn't erode the integrity of the solution. Make AI governance and observability real in the build ensuring audit logging, human-in-the-loop escalation points, and monitoring frameworks are implemented as designed, not deprioritized under delivery pressure. Bridge architecture and engineering with clients running working sessions that move from whiteboard to build, giving clients confidence that technical decisions are being executed with rigor. Build repeatable delivery capability turning implementation experience into reference patterns, accelerators, and standards that make the next delivery faster and better.