Machine Learning Engineer
il y a 2 semaines
Machine Learning Engineer (MLOps) | €900 Per Day | Remote | Long Term Contract Location: Remote in Belgium Employment Type: Full-Time, Long Term Contract Role Seniority Level: Senior ML Engineer Salary: €900 Per Day, Long Term Contract 3+ Years About the Role We are looking for a highly skilled Machine Learning Engineer with strong expertise in, MLOps, Python, Large Language Models (LLMs), and Azure Machine Learning. In this role, you will design, build, and deploy scalable machine learning solutions, enabling the team to deliver advanced AI capabilities across the organization. This role is for an industry leading SaaS business that specialise in optimizing financial trading development models. They are growing due to company success. You will work closely with data scientists, software engineers, and cloud architects to operationalize models, streamline workflows, and contribute to the development of production-grade ML and LLM solutions. Key Responsibilities Develop, deploy, and maintain ML models and pipelines with a focus on production-ready MLOps practices. Build and manage ML workflows using Azure Machine Learning (training, inference, model registry, pipelines). Fine-tune, optimize, and evaluate Large Language Models (LLMs) for business use cases. Collaborate with cross-functional teams to design scalable ML architectures and implement integration points with applications and services. Implement model monitoring, observability, and automated retraining strategies. Develop high-quality Python code for ML pipelines, feature engineering, evaluation, and automation. Optimize compute usage, experiment tracking, and deployment workflows within Azure. Ensure compliance with security, privacy, and responsible AI guidelines. Required Skills & Experience 3–5+ years of experience as a Machine Learning Engineer or similar role. Strong proficiency in Python and ML-related libraries (e.g., PyTorch, TensorFlow, scikit-learn). Hands-on experience with Azure Machine Learning, including pipelines, endpoints, compute clusters, and model registry. Experience with MLOps tools and workflows, such as CI/CD for ML, model versioning, data versioning, MLflow, or similar. Practical experience fine-tuning or working with LLMs, transformers, or other NLP models. Strong understanding of cloud-native architectures, containers, and orchestration (Docker, Kubernetes/AKS). Experience with REST APIs and integrating ML models into production systems. Solid grasp of ML lifecycle best practices, including data validation, evaluation methodologies, and monitoring. Strong communication and collaboration skills. Nice-to-Have Qualifications Familiarity with RAG (Retrieval-Augmented Generation) patterns or LLM application frameworks (LangChain, Semantic Kernel). Experience implementing Responsible AI practices. Knowledge of distributed training or model optimization techniques. Background in DevOps, infrastructure-as-code (Terraform/Bicep), or cloud security. Experience in Agile environments. What We Offer Opportunity to work with cutting-edge AI/ML and LLM technologies. Strong engineering culture with influence over architecture and ML strategy. Access to cloud resources and tooling for experimentation and innovation. Competitive salary + benefits package. Professional development, certifications, and career growth opportunities. Collaborative team environment focused on modern ML engineering best practices.
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