Generative AI Software Engineer Specialist

Who We Are

Accenture Song is the world's largest tech-powered creative group — a fusion of creativity, technology, intelligence, and experience. Within Song, our practice helps iconic brands reinvent the products, services, and spaces that define their relationship with customers. We work across the full spectrum: from defining product strategy and crafting human-centred digital experiences to engineering the AI-powered platforms and design systems that bring them to life at scale.

We operate like a studio embedded inside a global consultancy: small, high-craft teams with a direct line between design decisions and shipped products. With 70 Song Studios and more than 4,000 designers and makers across six continents, our Design & Digital Products team brings together specialists in Product Design, Product Management, Engineering, Experience Design, and Content Design — all united by a belief that the best digital products make people's lives genuinely better.

For us, AI is not a feature we bolt on — it is the operating model we build around. We are actively shaping what it means to design, build, and deliver AI-native products at enterprise scale, and we are looking for engineers who want to be at the centre of that reinvention.

Who You Are

You are an AI Native Product Engineer with demonstrated experience building cloud-native systems and shipping production‑grade AI solutions. You combine hands‑on engineering depth with the creativity and product intuition to shape solutions that make sense for real users and enterprises. You’re as comfortable writing production code as you are whiteboarding system designs, facilitating a client workshop, or mapping a product roadmap. You thrive in ambiguity and act as a bridge between vision and execution.

You do not treat AI as a toolset to learn on the side. It is your primary medium. You architect around large language models, retrieval systems, and multi‑agent orchestration the way previous generations of engineers built around microservices — with rigour, craft, and a clear eye on what will actually work in production. You translate complex AI behaviour into tangible user experiences that non‑technical stakeholders can understand and trust.

The Work

You will embed directly with clients as a technologist, product thinker, and trusted engineering partner. Working within cross‑functional Song studio teams — alongside Product Designers, Experience Designers, Product Managers, and Strategists — you will lead the engineering of AI‑native digital products from concept through to production. You will shape not only what gets built but how it gets built, setting the technical standard for AI‑native delivery within the practice. This is not a back‑office role. You will be in the room with clients, translating AI possibilities into product realities, and in the codebase, making those realities work.

AI‑Native Product Engineering

• Product Orchestration: Partner with business and technical stakeholders to frame problems, align priorities, and translate needs into orchestrated AI workflows.
• Agent Architecture & Engineering: Design and engineer enterprise‑ready AI agents — including retrieval, orchestration, policy‑based routing, tool invocation, evaluation harnesses, and lifecycle observability.
• Human‑AI Experience Engineering: Translate complex AI behaviours (confidence states, reasoning traces, agentic actions, fallback flows) into transparent, controllable, and accessible user experiences — in close collaboration with Experience Designers and Product Designers.
• AI Platform Integration: Build robust abstraction layers across AI providers (Anthropic Claude, OpenAI, Google Vertex AI, open‑source models) to create portable, multi‑provider pipelines that reduce client lock‑in and maximise flexibility.
• AI Design System Contributions: Contribute to AI‑native component libraries and design system patterns — error states, human‑in‑the‑loop interactions, progressive disclosure of AI reasoning — ensuring consistency and scalability across client products.

Cloud‑Native Engineering & Delivery

• Scalable Systems: Apply containerisation (Kubernetes, Docker), microservices, serverless, and event‑driven architectures to ship AI‑native systems that perform reliably at enterprise scale.
• Production Discipline: Own CI/CD pipelines, infrastructure‑as‑code (Terraform, Helm), monitoring, and debugging. Deploy to production and take responsibility for what you ship.
• Rapid Prototyping: Build working prototypes and proof‑of‑concept systems that close the gap between design vision and technical reality — using AI tooling actively to accelerate your own delivery.

Client Partnership & Practice Building

• Trusted Advisor: Serve as the primary technical voic