I&P - GN - SONG - AI & Data - Platforms - Full Stack Engineering - Manager

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.
Domain Focus
Candidates should bring full stack solution leadership and enterprise application delivery experience in one or more of the following domains: Marketing - campaign and content platforms, customer data applications, audience activation, personalization, measurement, and next-best-action experiences
Sales - sales productivity, recommendation, forecasting and revenue intelligence applications, customer or outlet prioritization, and route-to-market platforms
Commerce - product discovery, search and recommendations, catalog, pricing and promotions, order and inventory experiences, composable commerce, and customer portals
Service - conversational interfaces, agent assist, knowledge and case management, contact center applications, self-service, and operations automation
Design & Digital Products - enterprise web and mobile products, AI copilots, workflow platforms, experimentation, product analytics, and reusable digital services
Who We Are Looking For
Mandatory
Bachelor's or Master's degree in Computer Science, Information Technology, Software Engineering, Engineering, or a related discipline. MBA from a top institution is advantageous.
8-12 years of progressive experience in full stack application engineering and digital product delivery, including significant solution leadership and client-facing accountability.
Strong full stack depth and current hands-on credibility across at least one modern front-end framework and one enterprise back-end stack; able to prototype, review code, and resolve critical engineering issues.
Proven success leading enterprise applications from discovery and architecture through build, integration, testing, release, adoption, and production stabilization.
Strong application architecture foundations covering microservices, modular monoliths, API-first design, event-driven integration, domain-driven design, distributed systems, and reusable platform components.
Solid experience with cloud-native engineering on Azure, AWS, or GCP, including containers, Kubernetes or serverless patterns, managed services, infrastructure as code, and environment management.
Experience with relational and NoSQL databases, caching, search, messaging, object storage, transactional design, data consistency, and application data modelling.
Strong command of software quality and operations - automated testing, code quality, CI/CD, DevSecOps, observability, SRE principles, release management, incident response, and performance engineering.
Strong knowledge of application and API security, identity and access management, secure coding, privacy, secrets, encryption, threat modelling, and OWASP risks.
Data Engineering know-how sufficient to architect application-to-data-platform integration, including SQL, data models, data contracts, ETL/ELT, batch or streaming patterns, quality, lineage, and lakehouse or warehouse consumption.
ML/AI Engineering know-how sufficient to lead application integration with model and agent services, including inference APIs, RAG, embeddings and vector search, prompts and structured outputs, evaluation, guardrails, versioning, observability, and feedback loops.
Awareness of Responsible AI, AI security, human oversight, failure handling, explainability needs, and user-experience patterns for trustworthy AI-enabled applications.
Strong stakeholder engagement and consulting skills - ability to translate business and user needs into architecture choices, delivery plans, value narratives, and measurable outcomes.
Proven delivery leadership covering estimation, resource planning, delivery governance, dependencies, risks, quality, release readiness, and project economics.
Experience leading multidisciplinary and geographically distributed teams, reviewing architecture and code, mentoring engineers, and creating an inclusive high-performance culture.
Experience with Agile and product-centric delivery, backlog prioritization, design collaboration, architecture decision records, and continuous improvement.
Good to Have
Product management, design thinking, service design, or user-experience leadership experience relevant to digital product adoption.
Experience with mobile or cross-platform development, progressive web applications, composable platforms, headless architectures, or digital experience platforms.
Depth in API management, event streaming, Kubernetes, service mesh, platform engineering, FinOps, performance engineering, or site reliability engineering.
Recognized cloud, architecture, security, application development, data platform, or AI platform certifications.
Platform exposure to Databricks, Snowflake, Microsoft Fabric, Azure AI Foundry, Amazon Bedrock, SageMaker, Vertex AI, vector databases, or enterprise AI gateways.
Experience contributing to pursuits, solution demonstrations, reusable accelerators, reference architectures, communities of practice, or engineering thought leadership.
What We Are NOT Looking For Front-end-only, back-end-only, or packaged-platform specialists without credible end-to-end application architecture and delivery experience.
Pure Delivery or Program Managers without substantive full stack architecture, engineering, integration, code-review, and production problem-solving depth.
Architecture-only profiles that are disconnected from implementation, automated testing, DevSecOps, release management, and operational outcomes.
Pure Data Engineering, Data Science, or ML Engineering leaders without experience turning data and AI capabilities into enterprise-grade digital products.
Low-code, legacy application maintenance, or platform-configuration profiles without modern cloud-native engineering and transformation experience.
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