Summary
Andersen is hiring an ML Engineer for a project in cloud-native development, data engineering, and AI innovation with a leading tech consulting partner (20+ years in cloud, data, and generative AI).
The customer is a technology consulting partner focused on cloud modernization, data and analytics, and generative AI solutions. With over twenty years of experience, it supports organizations in migrating legacy systems, adopting cloud-native applications, and building scalable, secure architectures on leading platforms such as AWS. Its expertise spans SaaS development, DevOps, data modernization, and IT asset management, enabling clients to improve efficiency and accelerate business outcomes.
The project is focused on cloud-native application development, advanced data engineering, and AI-driven innovation. It leverages agentic and generative AI, computer vision, event-driven architecture, and IoT-powered automation to deliver cutting-edge digital solutions.
Responsibilities
- Architecting and operationalizing end-to-end AI/ML solutions in AWS, integrating scalable data pipelines, model training, deployment, monitoring, and governance using SageMaker, Lambda, Step Functions, and CloudWatch.
- Designing and implementing machine learning models for classification, regression, clustering, and recommendation systems.
- Developing and optimizing deep learning architectures using TensorFlow and PyTorch.
- Performing data preprocessing, feature engineering, and model evaluation with statistical and ML techniques.
- Deploying ML models into production using Amazon SageMaker, leveraging containerization support.
- Designing and deploying GenAI applications with Amazon Bedrock, leveraging foundation models from providers like Anthropic and AWS.
- Training, fine-tuning, and deploying custom LLMs using Amazon SageMaker, including SageMaker JumpStart and SageMaker Pipelines for automated workflows and production monitoring.
- Managing model versions and lifecycle with Amazon SageMaker Model Registry and Databricks Model Registry to ensure traceability and reproducibility.
Requirements
- Experience as a ML Engineer for 2+ years.
- Proven ability to architect and operationalize full-cycle AI/ML solutions in AWS, including scalable data pipelines, model training, deployment, monitoring, and governance using services such as SageMaker, Lambda, Step Functions, and CloudWatch.
- Experience in building machine learning models for classification, regression, clustering, and recommendation systems.
- Proficiency in developing and optimizing deep learning architectures using frameworks like TensorFlow and PyTorch.
- Strong background in data preprocessing, feature engineering, and model evaluation using statistical and machine learning techniques.
- Hands-on experience deploying ML models into production environments using Amazon SageMaker, including containerization.
- Familiarity with designing and deploying Generative AI applications using Amazon Bedrock and foundation models from providers like Anthropic and AWS.
- Ability to train, fine-tune, and deploy custom LLMs using Amazon SageMaker, including the use of SageMaker JumpStart and SageMaker Pipelines for automated workflows and production monitoring.
- Experience managing model versions and lifecycle using Amazon SageMaker Model Registry and Databricks Model Registry to ensure traceability and reproducibility.
- Level of English – from Upper-Intermediate.
Reasons to join us
- 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 USD 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.
- English courses.
- Private health insurance and compensation for sports activities.
Join us!
