ML Platform Engineer: Production AI & LLMs

Amaris Consulting | Part of Mantu; Göteborgs kommun; On-site; SEK 700,000 - 1,100,000; Full time

Job summary

Amaris Consulting, part of Mantu, is seeking AI/ML engineers to develop, train and deploy production-oriented models and ML services. Focus areas include LLM applications, data pipelines and industrial use cases, with production-grade Python development and DevOps practices.

The role emphasizes hands-on ML engineering experience, not just BI or dashboards, and requires collaboration across data scientists, engineers and domain experts to ensure robust ML solutions.

Qualifications

• Strong professional Python development experience.
• Hands-on experience building or training models with PyTorch, TensorFlow or a comparable framework.
• Experience with data preparation, model training, evaluation and deployment.
• Experience developing software systems rather than only notebooks or ad hoc scripts.
• Understanding of production considerations such as testing, versioning, monitoring and deployment.
• Strong English communication skills.

Responsibilities

• Building and maintaining machine-learning pipelines from data preparation through deployment.
• Training, evaluating and improving machine-learning models.
• Developing Python-based data and software components.
• Integrating or fine-tuning large language models for practical applications.
• Deploying ML services using containers, cloud platforms or orchestration tools.
• Monitoring model performance and improving production reliability.
• Collaborating with engineers, data scientists and domain specialists.
• Contributing to MLOps, experiment tracking, model versioning and CI/CD.

Skills

Python development

ML model training

Model deployment

Data pipelines

English communication

LLM integration

CI/CD practices

Tools

Docker

Kubernetes

Cloud platforms

PyTorch / TensorFlow

Job description

Amaris Consulting, part of Mantu, is seeking AI/ML engineers to develop, train and deploy production-oriented models and ML services. Focus areas include LLM applications, data pipelines and industrial use cases, with production-grade Python development and DevOps practices.

The role emphasizes hands-on ML engineering experience, not just BI or dashboards, and requires collaboration across data scientists, engineers and domain experts to ensure robust ML solutions.