Data & AI Strategy Enterprise AI Architect Manager/Senior Manager

Are you ready to design and scale the Data \& AI backbone of next\-generation intelligent enterprises?

At Accenture, we are reinventing organizations through data, cloud, and artificial intelligence—helping clients unlock measurable value through scalable AI platforms and advanced data ecosystems.

We are looking for a Data \& AI Platform Lead – Enterprise AI Architect to drive the architecture, implementation, and evolution of enterprise\-grade AI and Data platforms. This role combines deep expertise in cloud\-native architectures, AI enablement, and large\-scale data ecosystems with strong strategic vision and stakeholder leadership.

You will be responsible for designing AI\-ready platforms that support advanced analytics, machine learning, real\-time processing, and intelligent applications at scale—ensuring performance, security, governance, and long\-term scalability.

Key Responsibilities

• Lead the architecture and evolution of enterprise Data \& AI platforms in cloud environments (AWS, Azure, GCP).


• Design scalable architectures enabling:

+ Advanced analytics and ML workloads
+ Real\-time data processing
+ AI/ML model lifecycle management
+ API\-driven AI services

• Define reference architectures for AI\-enabled digital platforms.


• Design and govern data ecosystems including ingestion, storage, transformation, and serving layers.


• Integrate containerized AI workloads using Kubernetes and microservices\-based architectures.


• Ensure security, compliance, and governance across AI and data platforms.


• Align AI and data architecture with enterprise digital and business strategy.


• Lead cross\-functional teams across Data Engineering, ML Engineering, DevOps, and Security.


• Support large transformation programs and AI\-driven modernization initiatives.


• Drive innovation in AI platform capabilities (automation, MLOps, AI governance frameworks).

How does the ideal candidate look like:

• 6\+ years of experience in Data, Cloud, and AI architectures.


• Proven experience leading large\-scale Data \& AI platform implementations.


• Strong expertise in:

+ Cloud platforms (AWS / Azure)
+ Kubernetes and containerized environments
+ Microservices and API\-first architectures
+ Enterprise data platforms and AI ecosystems

• Experience designing AI\-ready infrastructures supporting ML at scale.


• Solid understanding of:

+ MLOps frameworks
+ Model lifecycle governance
+ Data architecture patterns (lakehouse, medallion, distributed systems)

• Experience in regulated or complex industries (Pharma, Banking, Public Sector) is a plus.


• Strong business alignment and executive stakeholder management.


• Experience contributing to RFPs and strategic solutioning.


• Fluency in English (required); international exposure is a plus.

The position is based in Barcelona or Madrid and follows a hybrid work model, with some days working from home and others in the office, where you can create interesting synergies with the rest of your team. It is essential to reside in Spain and have a work permit in Spain.

Technical Skills

• Programming: Python (mandatory), SQL.


• Cloud \& AI Platforms: AWS, Azure, GCP


• Containerization: Kubernetes (K8s), Docker


• AI Ecosystems: ML platform architecture, MLOps, AI lifecycle governance


• Data Architecture: Lakehouse, distributed data processing, real\-time pipelines


• APIs \& Microservices: Scalable AI service exposure


• DevOps \& Automation: CI/CD, infrastructure\-as\-code


• Security \& Compliance: Enterprise\-grade AI governance

Strategic \& Leadership Skills

• AI \& Data Strategy Definition


• Enterprise Architecture Alignment


• Global Program Leadership


• Stakeholder \& Executive Communication


• Business Case \& Value Realization


• Strategic thinking with hands\-on technical depth


• Structured problem\-solving in high\-complexity environments


• Cross\-functional and cross\-cultural leadership


• Innovation mindset

\#LI\-EU

Este anuncio proviene de indeed. Ver anuncio original ↗