MLOps / Cloud Engineer (AWS)

Ориентир по рынку≈ 3 900 – 4 500 $наша приблизительная оценка по 22 вакансиям этой роли и грейда, у работодателя вилка не указана

УдалённоSeniorПолная занятостьопубликовано 3 ч назад

≈ 3 900 – 4 500 $наша приблизительная оценка

Описание

Разбор

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История

Вакансия на английскомПереведёт заголовок и описание вакансии на русский

Чем предстоит заниматься

Design and set up AWS architecture for the ML pipeline: image ingestion, inference, storage of images, metadata and results

Build CI/CD pipelines in GitLab for application and model components

Implement Infrastructure as Code with Terraform

Set up development, pre-production and production deployment targets

Automate provisioning and controlled teardown of test and pre-production environments

Containerize services and integrate S3 and RDS

Set up pipeline monitoring, logging and operational traceability

Document infrastructure and deployment for knowledge transfer

Наши требования

Experience in DevOps / Cloud for 5+ years, with AWS as the primary cloud platform

Hands-on experience deploying ML models or Python services on AWS, including SageMaker, inference endpoints, and Step Functions

Experience with MLOps tooling, including MLflow or similar solutions

Hands-on experience using Terraform in real-world projects

Experience building and managing CI/CD pipelines using GitLab CI (GitHub Actions is also acceptable)

Experience with Docker and container deployment on AWS, including ECS, EKS, or Lambda

Experience working with S3 and RDS in delivered environments

Experience setting up and managing multi-environment infrastructure, including dev, pre-prod, and prod

Level of English – from Intermediate+ and above

Experience building infrastructure from scratch for new products

Experience working with hybrid on-premise and cloud environments

Knowledge of security and compliance practices, including ISO 27001 or similar standards

Experience working with GPU instances and managing cloud costs

Мы предлагаем

Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc

The opportunity to change the project and/or develop expertise in an interesting business domain

Job conditions – you can work both fully remotely and from the office or can choose a hybrid variant

Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee

The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities

Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated

Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies)

Certification compensation (AWS, PMP, etc)

Referral program

Private health insurance and sports compensation, depending on the type of employment

Дополнительно

Andersen is hiring an MLOps / Cloud Engineer (AWS) for a project developing an ML-powered image analysis platform to automate defect detection and improve industrial quality control

Our customer is an international company providing specialized industrial materials and technology solutions to businesses across multiple sectors. Combining technical expertise with research and development capabilities, the organization helps customers improve manufacturing processes, enhance product quality, and increase operational efficiency. Its activities focus on delivering innovative solutions, supporting sustainable industrial practices, and adapting to evolving technological and market requirements

The project is focused on developing and enhancing an image analysis platform for quality control in electroplating and surface finishing processes. It leverages Machine Learning and Computer Vision to automate defect detection, image segmentation, feature measurement, and comparison with expert reference values, enabling accurate, consistent, and objective quality assessments