ai engineer for enterprise AI applications

HireHi; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flexible working hours

Home-office option

30 days annual leave

Sabbaticals up to 1 year

Global health coverage

Job summary

NTT DATA ищет инженера ML/GenAI для разработки и внедрения проектов в сферах страхования, финансов, промышленности и автомобилестроения. Ваша задача — проектировать конвейеры данных, обучать и разворачивать модели, интегрировать AI в существующие решения и обеспечивать их устойчивость и мониторинг.

Вы будете работать с командой дата-сайентистов, инженеров MLOps/LLMOps и архитекторов, чтобы превратить прототипы в надёжные продакшн-решения и поддерживать документацию и качество кода на всём пути.

Qualifications

• Глубокие навыки Python, чистый код, тестирование, Git и ревью кода.
• Опыт работы с ML-фреймворками: PyTorch, TensorFlow, scikit-learn; обучение и развёртывание моделей.

Responsibilities

• Разрабатывать, обучать и оценивать модели ML/GenAI для промышленных приложений.
• Проектировать конвейеры данных, обучающие и инференс-пайплайны; переход от PoCs к продакшену.
• Интегрировать AI-компоненты в существующие приложения; обеспечивать масштабируемость и устойчивость.
• Сотрудничать с Data Scientists, MLOps и архитекторами; обеспечить качество и документацию.

Skills

Python

Clean Code

Testing

Git

Code Reviews

Debugging

Communication

Tools

PyTorch

TensorFlow

scikit-learn

pandas

Spark

Beam

REST

FastAPI

Flask

Azure

GCP

AWS

Docker

Kubernetes

MLOps

LLMOps

Job description

Описание:

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.

Задачи:

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

Требования:

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

Условия:

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