Job Description

Responsibilities

- Design, develop and lead the implementation of advanced Artificial Intelligence and Machine Learning solutions for production environments, ensuring their scalability, reliability and maintainability.
- Define, implement and evolve MLOps platforms and pipelines that automate the entire lifecycle of models: training, validation, versioning, deployment, monitoring and governance.
- Collaborate closely with Data Science teams to industrialize models, transforming prototypes and notebooks into high-performance productive services.
- Design and develop APIs, microservices and architectures aimed at integrating AI models and LLMs with business applications and business processes.
- Lead the adoption of good software engineering practices, including automated testing, code review, technical documentation, observability, and modular design.
- Design and implement CI/CD processes for AI applications and models, promoting automation and continuous delivery.
- Define and implement model monitoring strategies, including performance metrics, drift detection, data quality, explicability and alerting.
- Integrate AI solutions with data platforms, cloud architectures and business ecosystems, ensuring high standards of security, performance and resilience.
- Participate in the design of reference architectures for AI solutions, establishing technical standards, development patterns and best practices within the organization.
- Act as a technical reference, providing mentoring to junior profiles and collaborating in technological decision-making and the evolution of the AI platform.
- Work in multidisciplinary teams under agile methodologies, collaborating with business, engineering, architecture and operations areas to maximize the impact of AI solutions.

Requirements

- More than 5 years of experience designing, developing and industrializing Artificial Intelligence, Machine Learning or Generative AI solutions in business environments.
- Strong experience in software engineering, applying principles of design, architecture, automated testing, code review, documentation and good development practices.
- Mastery of Python and the AI/ML ecosystem, including frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers and libraries for Generative AI.
- Demonstrable experience in designing and implementing MLOps platforms and pipelines that cover the full lifecycle of AI models and applications.
- Advanced knowledge of version control with Git and of integration and continuous deployment (CI/CD) processes.
- Experience in containerization and orchestration of applications using Docker and Kubernetes.
- Experience developing and deploying AI services using REST APIs, microservices, batch processing and event-oriented architectures.
- Experience in cloud architectures on Azure, AWS or Google Cloud Platform, taking advantage of native services for AI, data and deployment.
- Knowledge in integrating AI solutions with data platforms, distributed architectures and business ecosystems.
- Experience collaborating with multidisciplinary teams and ability to lead technical decisions, mentor other engineers and communicate complex solutions to different profiles.

It will be especially valued

- Experience in Generative AI, including the development of applications based on LLMs, RAG (Retrieval-Augmented Generation), intelligent agents and orchestration using frameworks such as LangChain, LlamaIndex or Semantic Kernel.
- Experience working with AI platforms and MLOps such as Azure AI Foundry, Azure Machine Learning, MLFlow, Kubeflow, Vertex AI, Amazon SageMaker or equivalent platforms.
- Knowledge of monitoring, observability and evaluation of AI applications, including performance metrics, quality, model drift, traceability and tools such as Prometheus, Grafana or OpenTelemetry.




- Experience with Infrastructure as Code (Terraform, Bicep, ARM, CloudFormation or similar) and automation of cloud environments.
- Knowledge of modern data architectures, distributed processing and tools such as Spark, Databricks or equivalent platforms.
- Experience defining reference architectures, technical standards and good practices for AI solutions in production.
- Knowledge of aspects related to AI governance, security, privacy, responsible AI and regulatory compliance.
- Relevant certifications in cloud or AI technologies, such as Microsoft Azure AI Engineer, Azure AI Foundry, AWS Machine Learning Specialty, Google Professional Machine Learning Engineer or equivalent.

Additional Information

What do we offer you?

Environment and flexibility

- International, dynamic and collaborative environment.
- T-Social: social initiatives (sports, community, health,...).
- Hybrid work model (teleworking/face-to-face).
- Flexible schedule.
- 4 weeks of teleworking per year.

Professional Development

- Tailor-made training: access to Coursera so you can learn what you want, when you want.
- Weekly language classes (English, Spanish and German).
- Global Mentoring Sessions and Experience Days.

Compensation and benefits

- Adaptable compensation plan (health insurance, restaurant tickets, day care, transportation).
- Telemedicine.
- Life and accident insurance.
- Social fund.

Wellness and leisure

- More than 26 working days of vacation per year.
- Free access to specialists (health, wellness and legal advice).
- 100% of the salary in case of medical leave.

And many more advantages of being part of T-Systems! If you are looking for a new challenge, do not hesitate to send us your CV. Join our team! T-Systems Iberia will only process the CVs of applications that meet the requirements specified for each offer.

Your Task

This role involves designing, developing, and leading the implementation of advanced Artificial Intelligence and Machine Learning solutions for production environments, ensuring their scalability, reliability, and maintainability. You will define, implement, and evolve MLOps platforms and pipelines that automate the entire lifecycle of models.

Responsibilities

- Design, develop and lead the implementation of advanced Artificial Intelligence and Machine Learning solutions for production environments, ensuring their scalability, reliability and maintainability.
- Define, implement and evolve MLOps platforms and pipelines that automate the entire lifecycle of models: training, validation, versioning, deployment, monitoring and governance.
- Collaborate closely with Data Science teams to industrialize models, transforming prototypes and notebooks into high-performance productive services.
- Design and develop APIs, microservices and architectures aimed at integrating AI models and LLMs with business applications and business processes.
- Lead the adoption of good software engineering practices, including automated testing, code review, technical documentation, observability, and modular design.
- Design and implement CI/CD processes for AI applications and models, promoting automation and continuous delivery.
- Define and implement model monitoring strategies, including performance metrics, drift detection, data quality, explicability and alerting.
- Integrate AI solutions with data platforms, cloud architectures and business ecosystems, ensuring high standards of security, performance and resilience.




- Participate in the design of reference architectures for AI solutions, establishing technical standards, development patterns and best practices within the organization.
- Act as a technical reference, providing mentoring to junior profiles and collaborating in technological decision-making and the evolution of the AI platform.
- Work in multidisciplinary teams under agile methodologies, collaborating with business, engineering, architecture and operations areas to maximize the impact of AI solutions.

Your Profile

We are seeking a highly experienced AI/ML Engineer with extensive expertise in designing, developing, and industrializing advanced Artificial Intelligence and Machine Learning solutions. The ideal candidate should have strong software engineering skills, deep knowledge of MLOps platforms, cloud architectures, and the ability to lead technical decisions while mentoring other engineers.

Must Have Skills

- More than 5 years of experience designing, developing and industrializing Artificial Intelligence, Machine Learning or Generative AI solutions in business environments
- Strong experience in software engineering, applying principles of design, architecture, automated testing, code review, documentation and good development practices
- Mastery of Python and the AI/ML ecosystem
- Experience with frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers
- Experience with libraries for Generative AI
- Demonstrable experience in designing and implementing MLOps platforms and pipelines that cover the full lifecycle of AI models and applications
- Advanced knowledge of version control with Git
- Advanced knowledge of integration and continuous deployment (CI/CD) processes
- Experience in containerization and orchestration of applications using Docker and Kubernetes
- Experience developing and deploying AI services using REST APIs
- Experience with microservices architecture
- Experience with batch processing
- Experience with event-oriented architectures
- Experience in cloud architectures on Azure, AWS or Google Cloud Platform, taking advantage of native services for AI, data and deployment
- Knowledge in integrating AI solutions with data platforms, distributed architectures and business ecosystems
- Experience collaborating with multidisciplinary teams
- Ability to lead technical decisions
- Ability to mentor other engineers
- Ability to communicate complex solutions to different profiles

Nice to Have Skills

- Experience in Generative AI, including the development of applications based on LLMs
- Experience with RAG (Retrieval-Augmented Generation)
- Experience with intelligent agents and orchestration using frameworks such as LangChain, LlamaIndex or Semantic Kernel
- Experience working with AI platforms and MLOps such as Azure AI Foundry, Azure Machine Learning, MLFlow, Kubeflow, Vertex AI, Amazon SageMaker or equivalent platforms
- Knowledge of monitoring, observability and evaluation of AI applications, including performance metrics, quality, model drift, traceability and tools such as Prometheus, Grafana or OpenTelemetry
- Experience with Infrastructure as Code (Terraform, Bicep, ARM, CloudFormation or similar)
- Experience with automation of cloud environments
- Knowledge of modern data architectures
- Knowledge of distributed processing
- Experience with tools such as Spark, Databricks or equivalent platforms
- Experience defining reference architectures, technical standards and good practices for AI solutions in production
- Knowledge of aspects related to AI governance
- Knowledge of security, privacy, responsible AI and regulatory compliance
- Relevant certifications in cloud or AI technologies, such as Microsoft Azure AI Engineer, Azure AI Foundry, AWS Machine Learning Specialty, Google Professional Machine Learning Engineer or equivalent

📌 Senior AI Engineer (m/f/d) (Gijón)
🏢 T-Systems
📍 Gijón