ai engineer for enterprise AI applications

NTT DATA provides enterprise and technology services, AI, and digital infrastructure. Its AI Delivery business unit evaluates, designs, and addresses complex IT technology requirements across industries.

• Develop, train, and evaluate Machine-Learning and GenAI models for production enterprise applications in Insurance, Finance, Manufacturing, and Automotive, guiding them from concept to production
• Design and implement data preparation, feature engineering, training, and inference pipelines, ensuring a reliable transition from PoCs and MVPs to production AI solutions
• Integrate AI components, APIs, and services into existing applications and platforms; optimize performance, scalability, and fault tolerance; and ensure reliable operation
• Collaborate with Data Scientists, MLOps/LLMOps Engineers, and Architects to turn prototypes into robust, maintainable production solutions
• Ensure quality engineering through testing, reproducibility, versioning of code, models, and data, and clear technical documentation; establish monitoring, observability, and drift detection for AI systems
• Support customer projects, RFPs, and RFIs through technical consulting, effort estimates, and work package structuring
• Take ownership of subsystems, technical workstreams, engineering standards, workshops, and customer communications, depending on seniority

• Strong Python and modern software engineering skills, including Clean Code, testing, debugging, Git, and code reviews
• Practical experience with Machine-Learning frameworks such as PyTorch, TensorFlow, or scikit-learn, as well as model training, evaluation, optimization, and production deployment
• Experience developing data pipelines and feature engineering processes with pandas, Spark, Beam, or comparable toolchains; understanding of data quality
• Experience developing APIs and services such as REST, FastAPI, or Flask, integrating AI solutions into enterprise environments, and working with modern cloud and cloud-native architectures on Azure, GCP, or AWS
• Knowledge of containerization and deployment with Docker; Kubernetes knowledge is preferred
• Experience or a 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
• Ability to explain technical topics clearly to customers and stakeholders

• Flexible working hours with flexitime; travel time counts as working time
• Home-office option, with a possible compensation allowance and workplace equipment
• 30 Days of annual leave, plus December 24 and 31 as non-working days
• Sabbaticals of up to one year are possible
• Remote work and mobile work from EU countries are possible
• Benefit and compensation optimization programs, including company pension, JobRad, Jobticket, and Corporate Benefits discounts
• Health and preventive care programs, including extended continued pay during illness, occupational medical care, worldwide accident insurance including leisure accidents, and family services
• Internal and external training through NTT DATA Academy, including professional training, certifications, soft-skills training, and Udemy access
• Communities and initiatives focused on diversity, equity and inclusion, ESG, and knowledge sharing
• Team-oriented culture with significant scope to shape the work