Job description

Azure Principal MLOps Engineer with Cloud practical experience Location: Remote from Spain (Spanish employment contract) Client as a fast-growing tech startup offering an AI-powered platform that automates trade compliance and ESG processes. It helps businesses reduce risks, cut costs, and streamline documentation for global trade operations across various industries. Join a fast-scaling tech company on a greenfield AI infrastructure project. You'll play a foundational role in building, from scratch, the entire MLOps environment for a highly automated Azure-based AI platform used for large language model operations. Requirements: • 5+ years of experience as ML Engineer with solid hands-on experience with MLOps in cloud environments (ideally Azure) • Proven track record implementing infrastructure with AKS and containerized workloads. • Experience with Argo CD and Argo Workflows or similar GitOps tools. • Proficiency in setting up CI/CD pipelines and IaC(Terraform, Bicep or ARM templates). • Strong experience with MLFlow for tracking experiments and managing ML artifacts. • Skilled in Python for automation, scripting, and integration tasks. • Familiarity with PostgreSQL, especially with vector extensions (pgvector is a plus). • Experience with Azure Redis Cache and Azure Blob Storage. • Strong knowledge of cloud security best practices (secret management, SSO, encryption) • Detail-oriented with strong documentation and collaboration skills. Will be a plus: • Hands-on experience integrating OpenAI APIs or working with LLMs. • Familiarity with Azure DevOps, Microsoft Defender for Cloud, and web application firewalls. • Knowledge of Traefik or similar reverse proxy solutions. • Background in prompt observability or prompt engineering. • Experience with advanced monitoring tools for AI pipelines. • Certifications in Azure, Kubernetes, or MLOps-related fields. Technology stack: • Cloud & Infrastructure (AKS, Azure Virtual Network, Azure Load Balancer, Azure DNS, Azure Key Vault, AAD) • CI/CD & Automation (Argo CD, Argo Workflows, Terraform, Bicep, Azure-native tools) • MLOps & AI Tools (MLFlow, pgvector, Azure Cache for Redis, Azure Blob Storage, OpenAI API) • Development & Containerization (Helm, Docker, Python) • Monitoring & Observability (MLFlow, Azure Monitor, Grafana) Responsibilities: • Implement and deploy Azure-based MLOps infrastructure according to the established architecture. • Set up and manage Kubernetes (AKS), Argo CD, Argo Workflows, PostgreSQL with pgvector, MLFlow, Redis, and Blob Storage components. • Build CI/CD pipelines using IaC tools, enabling automated deployments and scaling. • Integrate MLFlow for experiment tracking, prompt management, and observability. • Implement secure secret management, database encryption, and SSO with Azure Active Directory. • Migrate and refactor existing repositories into modular services, following the planned structure. • Configure monitoring tools for AI model performance, prompt usage, and infrastructure health. • Collaborate closely with existing team to ensure smooth deployment and operations. • Document processes, configurations, and operational best practices for long-term maintainability. Why this position: • This is a hands-on role where you will implement a modern, well-thought-out architecture that is ready to go. • You will work with the latest tools in cloud, MLOps (Azure, Kubernetes, MLFlow, vector databases, and more) • Your work will have a clear, visible impact - everything you build will directly support AI features that go into production. • You will have a lot of freedom to shape how things get done, while still having a clear roadmap to follow.

Requirements