AI Native Transformation Enterprise Consultant

Job Description

We Are

Accenture is recognized as a global leader in AI and cloud transformation, helping businesses across industries migrate, manage, and optimize their cloud environments. Through partnerships with leading cloud providers such as Nvidia, AWS, Microsoft Azure, and Google Cloud, Accenture offers end-to-end services that drive innovation and business agility.

The Cloud Advisory Practice focuses on helping organizations define, plan, and implement innovative AI and cloud strategies that drive business value. Leveraging deep expertise across cloud platforms and technologies, this practice works collaboratively with clients to design scalable, secure, and resilient cloud environments. The practice offers guidance in key areas such as agentic AI infrastructure & hosting, modern cloud foundation, security and resiliency, full-stack Fin Ops, and cloud-native development approaches, ensuring that clients achieve agility, operational efficiency, and long-term growth.

By aligning AI and cloud initiatives with business goals, the practice helps organizations realize the full potential of cloud innovation while navigating industry-specific challenges and regulations

You Are

A Cloud Architect interested in solving some of the hardest problems in enterprise AI transformation-designing multi-agent systems that actually work, building composable architectures that blend AI and traditional distributed systems, and transforming established industries.

The Work

Most software developers, engineers, and architects get to build software systems. Very few get to transform how industries operate. We're combining AI technology, industry expertise, and entrepreneurial experience to fundamentally reimagine core business processes across financial services, healthcare, procurement, retail, and logistics. We partner with our clients to build better products and experiences at enterprise scale.

* We're doing this by leveraging agentic architectures, multi-agent orchestration patterns, and composable AI systems alongside proven distributed patterns (Event Sourcing, Event-Driven Architecture, Microservices, Domain-Driven Design, CQRS) and technologies such as (Claude API, Neo4j, Qdrant, Postgre

SQL, event streaming platforms, vector databases, cloud platforms) to build AI-native solutions for the enterprise.

* Most of our work fits what we call AI Transformation Decoupling: we design and build state-of-the-art agentic systems to wrap legacy cores, establish real-time feedback loops, add new AI-native functionality, and methodically transform existing systems into composable, event-driven architectures that support human-AI collaboration at scale.

* Before we change anything, we use AI agents to systematically understand what exists-mapping dependencies, analyzing git history, discovering hidden coupling, and identifying knowledge concentration-transforming months of manual analysis into days of comprehensive intelligence. This means transformations informed by reality, not assumptions, and changes that don't break production because we discovered the constraints first. You'll learn patterns proven in production, understand when each applies and why, and build systems that scale because they're architecturally sound-not just technically sophisticated.

* Our team is deeply hands-on, highly technical, and prides itself on being battle-hardened, lead-from-the-front AI transformation thought leaders

Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.

Qualification

Here's what you need

* Minimum of 2 years of hands-on experience building interesting and innovative applications, or equivalent open-source contribution (several projects as ongoing contributor).

* Minimum of 2 years of experience explaining complex AI concepts to a non-technical audience. If you can explain what RAG, semantic search, or multi-agent orchestration are to a layperson, you've got this.

* Minimum of 2 years of experience designing software systems that incorporate AI components-understanding when AI is appropriate, designing around its limitations, and creating…