1 day ago
Erfurt, Germany +12 moreSenior
Responsibilities
- Develop, train, and evaluate machine-learning and generative-AI models for productive enterprise applications.
- Design and implement data-preparation, feature-engineering, training, and inference pipelines and transition AI solutions from PoCs through MVPs to production.
- Integrate AI components, APIs, and services into existing applications and platforms while improving performance, scalability, and fault tolerance.
- Collaborate with Data Scientists, MLOps/LLMOps Engineers, and Architects to turn prototypes into robust, maintainable production solutions.
- Establish testing, reproducibility, code/model/data versioning, technical documentation, monitoring, observability, and drift detection for AI systems.
- Support customer projects, RFPs, and RFIs through technical consulting, estimation, work-package structuring, workshops, and customer communication.
Requirements
- Very good knowledge of Python and modern software engineering, including Clean Code, testing, debugging, Git, and code reviews.
- Practical experience with PyTorch, TensorFlow, or scikit-learn and with model training, evaluation, optimization, and production deployment.
- Experience developing data pipelines and feature-engineering processes with pandas, Spark, Beam, or comparable toolchains, together with an understanding of data quality.
- Experience developing APIs and services with technologies such as FastAPI or Flask and integrating AI solutions into enterprise environments.
- Knowledge of modern cloud and cloud-native architectures on Azure, GCP, or AWS.
- Experience with Docker-based containerization and deployment; Kubernetes is desirable.
- Experience or solid understanding of MLOps/LLMOps, monitoring, versioning, retraining, and production AI operating models.
- Experience or strong interest in generative AI and large language models, including RAG, embeddings, vector stores, prompt engineering, and guardrails.
- Business-fluent German and very good English, with the ability to explain technical topics clearly to customers and stakeholders.
Benefits
- Flexible working hours with flexitime, with travel time counted as working time.
- Home-office option, potentially including a compensation allowance and workplace equipment agreement.
- 30 days of annual leave plus December 24 and December 31 as non-working days.
- Sabbatical of up to one year and remote/mobile work options within the EU.
- Additional benefits including company pension, JobRad, Jobticket, Corporate Benefits discounts, health and preventive-care programs, family services, and worldwide leisure accident insurance.
- Internal and external professional development through the NTT DATA Academy, certifications, soft-skill training, and Udemy access.
- Collaborative culture, communities and initiatives, and substantial room for ownership and professional growth.
Tech Stack
Apache BeamApache SparkAWSAzureDockerFastAPIFlaskGitGoogle Cloud PlatformKubernetesPandasPythonPyTorchscikit-learnTensorFlow
Categories
About NTT DATA
NTT DATA is a Tokyo‑headquartered, publicly traded IT services firm within the NTT Group. It provides consulting, application development, system integration, cloud and enterprise application services, and managed/outsourcing services for large enterprises and public-sector organizations. The company operates in more than 50 countries and is listed on the Tokyo Stock Exchange.