Synthetica

is an industrial data science company that specializes on IoT, deep learning and AI in the Industrial Sectors. We believe in pushing the boundaries of what is possible, and we’re passionate about creating innovative solutions on real – world problems that drive progress and change through cutting edge- technology.

If you're ready to join a team that is making a significant impact on the future of this vital industry, we are growing and are looking for a Mid DevOps/ MlOps Engineer

who will become an integral part of our product delivery process.

Responsibilities

• Design and maintain Azure cloud infrastructure using Terraform, ensuring reproducibility and scalability across environments
• Build and operate serverless retraining pipelines for both open-source and closed-source multimodal models
• Develop and manage CI/CD workflows via GitHub Actions, automating testing, deployment, and model release processes
• Automate infrastructure provisioning, monitoring, and alerting to ensure system reliability and uptime
• Collaborate with AI and data teams to integrate model training, evaluation, and serving workflows into unified MLOps pipelines
• Maintain secure, cost-efficient cloud resource management across development and production environments
• Document infrastructure architecture, runbooks, and pipeline configurations to support team-wide operational clarity

Requirements

• 2–4 years of experience in a DevOps, MLOps, or cloud infrastructure role
• Strong Bash scripting and Linux command-line proficiency
• Solid experience with Azure cloud infrastructure (AKS, Azure Functions, Storage, ACR, Key Vault)
• Hands-on experience with Terraform for infrastructure-as-code and environment provisioning
• Proven ability to design and maintain CI/CD pipelines using GitHub Actions
• Experience building serverless or event-driven automation pipelines
• Familiarity with containerization technologies (Docker, Kubernetes)
• Strong communication skills and ability to collaborate across engineering and data science teams

Nice to Have

• Experience orchestrating ML workflows with tools such as Azure ML, Prefect, or similar platforms
• Familiarity with working alongside both open-source (e.g., LLaMA, Mistral) and closed-source (e.g., OpenAI, Azure OpenAI) multimodal models
• Understanding of model versioning, experiment tracking, and evaluation frameworks
• Exposure to data engineering concepts and document processing pipelines
• Interest in the intersection of MLOps and generative AI — eagerness to grow in this hybrid space
• Experience with monitoring stacks such as Prometheus, Grafana, or Azure Monitor



Benefits

• Competitive compensation & ticket restaurant card
• Flexible working schedule & extensive insurance plan
• Cutting-edge IT equipment and continuous training programs
• Coding assistants provisioning