Job Description

About Song - Data & AI

Accenture Song uses AI, proprietary customer data, and product platforms to help clients improve customer experience and drive measurable growth across marketing, sales, commerce, and service. From strategy through execution, Song Data & AI helps organizations build and operationalize advanced capabilities - covering customer data unification, predictive analytics, and Generative AI (including agentic use cases) - to enable smarter decisioning like personalization and next best action, faster content and experience delivery, and more effective commerce and customer engagement.

What's In It For You

Join a worldwide network of digital product, full stack, cloud, data, and AI leaders delivering secure, scalable applications for enterprise transformation.
Access world-class training, mentorship, and certifications across application architecture, cloud-native engineering, DevSecOps, product delivery, data platforms, and AI-enabled experiences.
Lead high-visibility engagements across Marketing, Sales, Commerce, Customer Service, and Digital Products - taking cloud-native and AI-enabled applications from solution design through enterprise adoption.
Shape Accenture's reusable application frameworks, design systems, service components, reference architectures, delivery standards, and AI experience patterns.

What You Will Do

As a Full Stack Engineering Manager, you will lead the solution design, implementation, and enterprise adoption of cloud-native and AI-enabled applications. You will combine full stack architecture and hands-on technical judgment with consulting and delivery leadership, integrating governed data platforms and ML/AI services into secure, scalable digital products.

Translate business and product goals into solution architecture, experience and integration designs, prioritized backlogs, technical roadmaps, delivery estimates, and measurable acceptance criteria.
Architect end-to-end application solutions spanning responsive front ends, back-end services, APIs, workflow or domain services, data stores, messaging, identity, and cloud runtime components.
Lead multidisciplinary teams building accessible user experiences, microservices, real-time workflows, enterprise integrations, and reusable application capabilities.
Define integration patterns for enterprise systems, APIs, event streams, governed data products, lakehouse or warehouse platforms, operational databases, and third-party services.
Lead the design of AI-enabled applications using predictive model endpoints, Generative AI, RAG, vector search, copilots, agents, multimodal services, feedback loops, and human-in-the-loop controls.
Establish engineering practices for modular design, code quality, automated testing, DevSecOps, CI/CD, infrastructure as code, containerization, environment promotion, observability, and production support.
Ensure application solutions meet requirements for security, privacy, Responsible AI, accessibility, scalability, performance, resilience, maintainability, interoperability, and cost.
Lead architecture, design, and code reviews; guide technology selection; resolve complex cross-stack issues; and maintain technical integrity through delivery.
Engage senior client stakeholders through discovery workshops, solution presentations, architecture walkthroughs, prototypes, demonstrations, and decision forums - communicating value, risks, dependencies, and trade-offs.
Manage delivery governance, resource planning, estimation, quality, dependencies, risks, issue resolution, release readiness, and project economics across application engagements.
Lead and mentor Consultants and Analysts across front-end, back-end, quality, cloud, data integration, and AI application engineering, while collaborating closely with design, Data Engineering, and ML/AI Engineering teams.
Contribute to proposals, reusable accelerators, reference architectures, design systems, engineering standards, and playbooks that scale full stack and AI application delivery across the practice.