AI Delivery Tech Lead, Song Service

Overview

In this role you will architect and deliver enterprise-scale AI systems as part of Accenture Song’s Service practice. You’ll shape transformational customer platform solutions, moving agentic products from prototype to production. You work across CCaaS, AI design, and delivery to create scalable, compliant experiences. You’ll lead engineering teams, set technical standards, and partner with clients to drive measurable business outcomes.

Pay / Benefits

• competitive basic salary
• annual performance bonus
• equity opportunities
• health and wellbeing benefits
• 30 days of leave plus 3 volunteering days
• flexible work policies and mobility

Responsibilities

• Architect and build agentic experiences for production.
• Hands-on CCaaS/AI design to scale customer experience platforms.
• Lead RAG pipelines, multi-agent orchestration, and evaluation frameworks.
• Advise clients on AI architecture choices, platform selection, and build vs buy decisions.
• Define delivery approaches and infrastructure strategies; engage senior client leaders.
• Lead engineering teams across complex, multi-system programmes and set governance.
• Collaborate with engineers, designers and strategy leads to deliver outcomes.
• Mentor and grow technical capability within the practice and support go-to-market activities.

Key requirements

• Strong engineering background with production AI experience in Python and similar languages.
• Track record of architecting/delivering RAG pipelines, multi-agent systems or LLM-based products at scale.
• Hands-on experience with major cloud AI platforms (AWS Bedrock, GCP Vertex AI or Azure AI Studio).
• Experience designing eval frameworks, red-teaming, and quality measurement for AI systems.
• Portfolio showing production-deployed AI in regulated/enterprise environments.
• Proven ability to lead engineering teams in agile, client-facing delivery.

• Strong technical leadership
• Clear, credible communication with senior stakeholders
• Ability to articulate architecture trade-offs under delivery pressure
• Agentic frameworks: LangGraph, LangChain, CrewAI, Agentforce
• Foundational models: Claude Code/Codex/Gemini, OpenAI, AWS Bedrock
• Vector databases: Pinecone, Weaviate, LlamaIndex, pgvector

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