AI Software Architect Bucuresti, Bucuresti, Romania AI Engineering Posted ago

Position: AI Software Architect Bucuresti, Bucuresti, Romania AI Engineering Posted 10 hours ago
Location: Germany

Hybrid## AI Software Architect Bucuresti, Bucuresti, Romania At Vodafone, we’re not just shaping the future of connectivity for our customers – we’re shaping the future for everyone who joins our team. When you work with us, you’re part of a global mission to connect people, solve complex challenges, and create a sustainable and more inclusive world. If you want to grow your career whilst finding the perfect balance between work and life, Vodafone offers the opportunities to help you belong and make a real impact.

As an AI Software Architect at Vodafone Romania, you will play a key role in designing and implementing cutting-edge artificial intelligence solutions. This role involves collaborating with data science and data engineering teams to ensure AI architectures align with corporate strategies and achieve business objectives.  The ideal candidate will have strong technical experience, excellent problem-solving skills, and the ability to lead and mentor AI teams.

AI Solution Development  
• Design, develop and implement end-to-end AI and genAI solutions for various business domains.  
• Design, develop and implement Agentic AI systems (Agentic AI):
Design, build and implement agentic AI systems that demonstrate autonomy, tool-use, reasoning, memory and planning capabilities.  
• Lead the implementation of AI models, algorithms and systems in production environments.

Analysis of IT requirements and trends  
• Analyzes and responds to clients' business requirements in order to build, adapt and harmonize AI solutions for IT  
• Analyzes the technologies specific to the communications industry, global IT development trends, and Artificial Intelligence, global IT and AI system development methodologies, market requirements as well as its development trends, proposes an IT solution in accordance with the business need, budget and available time requested by external clients

Architecture and Data Management  
• Defining efficient data architectures to feed machine learning models and facilitate transparent data sharing between systems.  
• Defining MCP and RAG (retrieval-augmented generation) architectures.

Infrastructure and Performance Optimization  
• Establish robust infrastructures, deployed in cloud environments, to ensure that AI models operate on accurate data, meet SLAs (Service Level Agreements) and deliver high performance.  
• Design MLOps Pipelines:
Develop, maintain and optimize automated CI/CD pipelines for deploying, monitoring and retraining models, ensuring their reliability and scalability.

Leadership and Collaboration  
• Coordinates, validates and guides AI Engineers and Prompt Engineers towards optimal solutions and implementations of AI projects.  
• Coordinates and sets the framework for calculating and evaluating the accuracy of implemented AI algorithms, together with the RAI Market Lead.  
• Provides technical guidance and mentoring to AI Engineers and Prompt Engineers.  
• Collaborates with enterprise architects, solution architects and stakeholders to ensure smooth integration of AI solutions.

Security and Compliance  
• Implementing best practices for protecting sensitive data and AI systems while maintaining compliance with industry standards.##
** Who you are
*
* Required:

o Minimum 6 years of experience designing, building and managing large-scale data applications, especially in Hadoop ecosystems (Cloudera, Spark, Hive/Impala, etc.).  o Hands-on experience with Artificial Intelligence (AI) frameworks and libraries, such as Tensor Flow, PyTorch and Scikit-learn.  o Strong knowledge of Generative Artificial Intelligence (GenAI), including foundational models, generative adversarial networks (GANs) and reinforcement learning.  o Expertise in software development using Python, Java or Scala (knowledge of other programming languages is an advantage).  

o Proven experience in implementing and deploying AI models in production environments.  o Deep knowledge of containerization and orchestration tools such as Docker and Kubernetes.  o Strong understanding of cloud environments (AWS, Google Cloud or Azure), microservices architectures and real-time analytics.  o Deep knowledge of core…