Design, develop and lead the implementation of advanced AI and Machine Learning solutions for production, ensuring scalability, reliability and maintainability.
Define, implement and evolve platforms and ML Ops pipelines that automate the full lifecycle of models: training, validation, versioning, deployment, monitoring and governance.
Collaborate closely with Data Science teams to industrialize models, turning prototypes and notebooks into high‑performance, productive services.
Design and develop APIs, microservices and architectures for integrating AI models and LLMs with business applications and processes.
Lead the adoption of software engineering best practices, including automated testing, code reviews, technical documentation, observability and modular design.

Key requirements

Design, develop and lead the implementation of advanced AI and Machine Learning solutions for production, ensuring scalability, reliability and maintainability.
Define, implement and evolve platforms and ML Ops pipelines that automate the full lifecycle of models: training, validation, versioning, deployment, monitoring and governance.
Collaborate closely with Data Science teams to industrialize models, turning prototypes and notebooks into high‑performance, productive services.
Design and develop APIs, microservices and architectures for integrating AI models and LLMs with business applications and processes.
Lead the adoption of software engineering best practices, including automated testing, code reviews, technical documentation, observability and modular design.

Jobs at T-Systems Iberia

3 active roles from this employer in the JobMatcher catalog.

All company jobs

Jobs at T-Systems Iberia

3 active roles from this employer in the JobMatcher catalog.

All company jobs

About the role

Company description T-Systems is part of the Deutsche Telekom group, with around 30,000 employees worldwide. We create technology with purpose to generate a positive social impact. We seek curious talent eager to learn, take on challenges and contribute ideas that transform our customers’ experience. We trust people: we offer autonomy, ongoing support and a collaborative environment where you can grow without limits. We are a single global team, guided by respect, integrity, and the passion to do better every day.

Job description

Responsibilities

Design, develop and lead the implementation of advanced AI and Machine Learning solutions for production, ensuring scalability, reliability and maintainability.
Define, implement and evolve platforms and ML Ops pipelines that automate the full lifecycle of models: training, validation, versioning, deployment, monitoring and governance.
Collaborate closely with Data Science teams to industrialize models, turning prototypes and notebooks into high‑performance, productive services.
Design and develop APIs, microservices and architectures for integrating AI models and LLMs with business applications and processes.
Lead the adoption of software engineering best practices, including automated testing, code reviews, 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, explainability and alertin

About the role

Company description T-Systems is part of the Deutsche Telekom group, with around 30,000 employees worldwide. We create technology with purpose to generate a positive social impact. We seek curious talent eager to learn, take on challenges and contribute ideas that transform our customers’ experience. We trust people: we offer autonomy, ongoing support and a collaborative environment where you can grow without limits. We are a single global team, guided by respect, integrity, and the passion to do better every day.

Job description

Responsibilities

Design, develop and lead the implementation of advanced AI and Machine Learning solutions for production, ensuring scalability, reliability and maintainability.
Define, implement and evolve platforms and ML Ops pipelines that automate the full lifecycle of models: training, validation, versioning, deployment, monitoring and governance.
Collaborate closely with Data Science teams to industrialize models, turning prototypes and notebooks into high‑performance, productive services.
Design and develop APIs, microservices and architectures for integrating AI models and LLMs with business applications and processes.
Lead the adoption of software engineering best practices, including automated testing, code reviews, 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, explainability and alertin

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