Contratação para uma empresa localizada na Suécia. Há necessidade de mudança para Gotemburgo.
Machine Learning Engineer (MLOps & Scalable Cloud AI)
Are you passionate about DevOps, automation, and bringing AI to production at scale? We’re
looking for a Machine Learning Engineer to lead scalable ML system design and deployment with a
focus on automation and production reliability
Key responsibilities:
Establish MLOps best practices and patterns for scalable ML deployment.
Design and build reproducible ML pipelines and model-serving infrastructure.
Manage and automate CI/CD for ML using GitHub Actions or Azure DevOps.
Operate in cloud-first environments (GCP or AWS), using tools like Vertex AI, DBT, Airflow, or
Kubeflow.
Implement observability (model monitoring, drift detection) and infrastructure-as-code
(Terraform, Helm).
Collaborate with Data Scientists, Engineers, and Analysts to move models from notebooks to
production.
Ensure ML workflows align with data governance, security, and compliance standards.
Contribute to LLM-based model serving and fine-tuning pipelines where applicable.
Academic degree in Computer Science, Engineering, or a related field.
Experience in Software Development/DevOps or related field.
Experience in ML engineering or MLOps in production settings.
Proficient in Python (OOP, testing, clean code, package management).
Experienced in cloud platforms - GCP and AWS.
Experienced with AWS services for ML deployment and infrastructure management, including SageMaker, CloudWatch, and IAM.
Experience developing RESTful APIs using FastAPI for model serving and inference endpoints, including integration with CI/CD and auth middleware.
Hands-on with CI/CD pipelines, Containerization (Docker, Kubernetes), Infrastructure as Code (Terraform, ArgoCD, etc.), MLFlow, DBT, and Airflow.
Experience in monitoring/observability strategies for production ML systems, including latency tracking, drift detection, and model version health using Prometheus, Grafana, or Vertex AI Model Monitoring.
Strong skills in SQL, data modeling, and scalable data pipelines.
Able to work in agile, cross-functional teams with clear communication and ownership mindset.
Strong analytical problem-solving skills and love for clean, maintainable systems.
Curious, experimental, and fast learner with excellent communication skills in English.
Experience with large language models and LLMOps pipelines is a plus.
Working knowledge aligned with GCP/AWS ML certification standards (Vertex AI, IAM, Dataflow) is a plus.