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

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 full stack, cloud, data, and AI engineers building secure digital products and AI-enabled enterprise applications.
• Access world-class training, mentorship, and certifications across modern front-end and back-end engineering, cloud-native development, DevSecOps, data platforms, and applied AI.
• Work on high-visibility engagements across Marketing, Sales, Commerce, Customer Service, and Digital Products - creating applications that make data, ML, Generative AI, and agentic capabilities usable at scale.
• Contribute to Accenture's reusable user-interface components, application services, integration patterns, reference implementations, testing assets, and engineering playbooks.

What You Will Do

As a Full Stack Engineering Consultant, you will own the hands-on design, build, testing, deployment, and support of assigned application modules and workstreams. You will create responsive user experiences and scalable services, integrate governed data and ML/AI capabilities, and guide junior team members within agreed architecture, security, and delivery standards.

• Analyze business, user, and technical requirements and translate them into user stories, acceptance criteria, API contracts, detailed designs, and executable engineering tasks.
• Build responsive, accessible, and reusable web interfaces using modern JavaScript or TypeScript frameworks such as React, Angular, Vue, or Next.js.
• Develop secure back-end services, microservices, and APIs using one or more enterprise stacks such as Java and Spring Boot, .NET, Node.js and NestJS, or Python and FastAPI.
• Implement application data models and persistence using relational and NoSQL databases, caching, search, object storage, and messaging services appropriate to the use case.
• Integrate applications with enterprise systems, identity services, APIs, files, event streams, and third-party platforms using synchronous and asynchronous integration patterns.
• Apply data engineering know-how to application delivery - including SQL, schema design, data contracts, app-specific ingestion or transformation, data quality checks, lineage awareness, and consumption of lakehouse or warehouse data products.
• Integrate ML and AI capabilities through model endpoints, Retrieval-Augmented Generation, embeddings and vector search, agent or tool APIs, structured outputs, feedback capture, and human-in-the-loop workflows in collaboration with ML/AI engineers.
• Implement engineering practices covering Git, code reviews, automated unit, integration and end-to-end testing, CI/CD, containerization, cloud deployment, configuration, and release management.
• Design and operate for security, privacy, accessibility, performance, resilience, observability, and cost; troubleshoot defects and production incidents across the application stack.
• Own an assigned module or workstream, coordinate day-to-day technical activities, review deliverables, and guide Analysts or Senior Analysts.
• Engage client product owners, designers, architects, data teams, and AI teams through requirement workshops, design walkthroughs, sprint reviews, demonstrations, and issue resolution.
• Contribute reusable code, components, technical documentation, reference implementations, and practice knowledge assets.