About the role

AI summarised

Full Stack LLM Developer at Accenture, responsible for designing and building end-to-end applications powered by Large Language Models (LLMs) to solve complex business problems. The role involves working across the full stack, integrating LLMs into user-facing workflows, and delivering secure, scalable AI solutions for enterprise clients.

BusinessFull-timeOther Functions

Key Responsibilities

Design and develop full stack applications that integrate LLMs into user-facing workflows and business processes
Implement back-end services and APIs for prompt orchestration, retrieval-augmented generation (RAG), and tool / API calling
Build responsive, intuitive front-end interfaces that enable users to interact with LLM-powered features effectively
Integrate with enterprise data sources, vector stores, and authentication/authorization mechanisms
Collaborate with architects, data engineers, and product owners to translate business requirements into technical designs
Implement observability, logging, and monitoring for LLM interactions and application performance
Apply secure coding practices and contribute to guardrails for responsible AI usage
Participate in code reviews, technical design discussions, and continuous improvement of engineering practices

Requirements

Experience in full stack application development
Strong proficiency in at least one back-end language/framework (e.g., Node.js, Python, Java, .NET)
Experience with modern front-end frameworks (e.g., React, Angular, or Vue)
Hands-on experience integrating with LLMs (e.g., Azure OpenAI, OpenAI, other enterprise LLM platforms)
Solid understanding of RESTful APIs, microservices, and event-driven or serverless architectures
Experience working with relational and/or NoSQL databases, and basic understanding of vector databases
Familiarity with cloud platforms (Azure, AWS, or GCP) and CI/CD pipelines
Strong problem-solving skills and ability to work in cross-functional, agile teams
Experience building retrieval-augmented generation (RAG) pipelines or LLM chat/workflow applications
Exposure to LLMOps concepts (prompt management, versioning, evaluation, and safety guardrails)
Knowledge of authentication/authorization standards (OAuth2, OpenID Connect) and enterprise security practices
Experience in regulated industries (e.g., banking, financial services, or compliance)
Familiarity with containerization and orchestration (Docker, Kubernetes)