20 publicaciones de empleo destacadas de azure data architect en md (¡Están contratando!)

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Senior Generative AI Tech Lead / Architect (m/f/d)

Siemens -
Madrid, Madrid provincia

Solicitar ahora

Detalles del empleo

Contrato indefinido | Jornada completa

Cualificaciones

TensorFlow
CI/CD
Azure
Derecho
Doctorado
Informática
Inglés
Máster
AWS
SoC
Docker
Machine learning
APIs
Cloud
Inteligencia artificial
Liderazgo
Python
Patrones de diseño

Descripción completa del empleo

Create a better #TomorrowWithUs

At Siemens, we build technology solutions to shape the world we live in. We transform industries and societies by combining the real and digital worlds. With over 300.000 of the world’s most forward-thinking minds and the power of a presence in more than 190 countries, we make a truly global impact.

Are you ready to be part of the change? Come join us!

Your mission will be…

The Senior Generative AI Tech Lead is a key member of the Siemens Cybersecurity (CYS) organization, operating at the intersection of technical leadership, Generative AI engineering, and strategic planning. This role sits within the CYS AI & Platforms team and carries a dual mandate: driving hands-on technical delivery of Generative AI solutions across cybersecurity and productivity use cases, while co-shaping the long-term AI strategy for the CYS organization.

The Senior Generative AI Tech Lead leads projects end-to-end — from solution design and architecture to prototyping and implementation — and acts as a key contributor to the CYS AI governance model, reusable blueprint development, and cross-team AI initiative coordination. She/He translates complex business and cybersecurity requirements into scalable, secure, and production-grade Generative AI solutions built on top of the Siemens foundation AI platform, which leverages Microsoft Azure.

Responsibilities

Lead projects end-to-end as Tech Lead: own solution design, architecture definition (4+1 Architectural View Model), prototyping, and implementation guidance across Generative AI use cases in cybersecurity and productivity increase domains.
Design and architect scalable Generative AI solutions leveraging techniques such as Retrieval-Augmented Generation (RAG), Agentic AI, prompt engineering, fine-tuning, and multi-modal AI, built on top of the Siemens CYS AI platform.
Support the CYS Platforms Department in defining and executing a long-term AI strategy, contributing to governance frameworks, portfolio visibility, and structured onboarding of AI use cases across CYS.
Develop reusable blueprints, architecture patterns, and guidelines for secure Generative AI components and common CYS use cases, promoting standardization, reuse, and faster time-to-implementation.
Support a shared vendor and technology strategy across CYS, contributing to the reduction of fragmentation and improving alignment on AI services and architectural choices (e.g., Azure OpenAI, LangChain, Hugging Face).
Collaborate with the CYS AI Strategy team on prioritizing AI initiatives, identifying overlaps across teams, and coordinating efforts in a structured and transparent way.
Enable a standardized operational support model by promoting best practices in AI monitoring, lifecycle management, ownership definition, and maintenance across CYS AI use cases.
Mentor and guide team members on Generative AI engineering practices, code quality, and solution design, fostering a culture of technical excellence.
Collaborate with cross-functional stakeholders — including cybersecurity analysts, platform engineers, and business owners — to gather requirements and translate them into AI-driven solutions.
Design AI experiments, evaluate results, and communicate findings clearly to both technical and non-technical audiences.
Apply advanced skills to resolve complex, cross-functional problems independently and with a high level of critical thinking.

We are looking for someone with…

BS/BA in Computer Science, Computer Engineering, Mathematics, or a related discipline; advanced degree (MSc/PhD) preferred, or equivalent combination of education and experience.
Typically 5+ years of successful work experience, with multiple years in AI/ML engineering, solution architecture, or a related technical leadership role.
Strong proficiency in Python as the primary programming language for AI/ML development; proven skills in structuring code and applying software design patterns.
Hands-on experience with Generative AI techniques and frameworks, including:
Retrieval-Augmented Generation (RAG)
Agentic AI / AI Agents (e.g., LangGraph, AutoGen, CrewAI, ArizeAI)
LLM orchestration frameworks (e.g., LangChain, LlamaIndex)
LLM APIs and model services (e.g., Azure OpenAI, OpenAI API, Hugging Face)
Prompt engineering and fine-tuning strategies
Proven experience with cloud platforms, with Microsoft Azure strongly preferred (e.g., Azure OpenAI Service, Azure AI Studio, Azure Machine Learning); AWS experience also valued (e.g., Amazon SageMaker, AWS Bedrock).
Experience designing and deploying production-grade AI/ML applications, including CI/CD pipelines, monitoring, and lifecycle management.
Demonstrated ability to lead technical initiatives and provide thought leadership across cross-functional teams.
Experience contributing to or defining AI governance frameworks, architecture standards, or technology strategies at team or department level.
Strong written and verbal communication skills in English, including professional maturity and presentation skills.
Demonstrated ability to work independently, follow and drive execution plans, and adapt quickly to a fast-paced environment.
Demonstrated ability to train and guide colleagues, promoting knowledge sharing and technical growth.
Preferred Knowledge/Skills, Education, and Experience
Microsoft Azure Certification is of advantage; e.g., Azure AI Engineer Associate, Azure Solutions Architect, Azure Data Scientist Associate.
Familiarity in Cybersecurity domains — such as threat detection, security operations, vulnerability management, or identity & access management — is a strong plus.
Familiarity with secure AI design principles, responsible AI practices, and AI risk management frameworks.
Experience with data engineering and integration frameworks; e.g., event-driven architectures, message queues, databases, and data pipelines.
Knowledge of containerization and orchestration technologies such as Docker or Kubernetes.
Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-Learn.
Understanding of AI portfolio management and structured approaches to initiative prioritization (e.g., roadmapping, OKRs, value assessment).
Experience working in or with Security Operations Centers (SOC) or cybersecurity platforms is of advantage.

Please attach your CV in English to your application.

#Siemens #Cybersecurity

Siemens is deeply committed to fostering a diverse and inclusive environment. We are proud to be an equal opportunity employer and strongly encourage applications from a wide array of talented individuals!

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