About Baronit:
At Baronit, we connect brilliant minds to shape the future of technology. As a passionate team of tech experts, we lead with innovation, expertise, and curiosity to help businesses grow and adapt to new opportunities. Our experts blend technical excellence, industry insight, and a strong commitment to delivering exceptional results across sectors such as Automotive, Airline, Fintech, Healthcare, Retail, Telecom, E-commerce, and more.
We are an IT consultancy company based in Gothenburg looking for an experienced Machine Learning Engineer to join our team.
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, Azure, 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.
Ideal candidate profile:
Academic degree in Computer Science, Engineering, or a related field.
7+ years of experience in Software Development/DevOps or related field.
4+ years of experience in ML engineering or MLOps in production settings.
Proficient in Python (OOP, testing, clean code, package management).
Experienced in cloud platforms - GCP, Azure, 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.
In addition to exciting projects, we also offer:
Flexible salary model – choose between a fixed salary or a revenue-based model where you receive X% of your client rate, with full transparency
Technical forums for continuous learning and knowledge sharing
Social activities to stay connected and engaged
Annual offsite conference for team bonding and inspiration
And above all – a great team spirit and a focus on enjoying the journey together