Machine Learning Engineer
1 week ago
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
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