Cloud Platform Engineer (Automation) (mwd)

Job summary

The role focuses on developing and deploying intelligent agents and automation solutions in a secure and scalable manner, integrating AI Engineering, Data Engineering, and Cloud Architecture. Key responsibilities include designing productive AI solutions on Databricks and Azure, implementing CI/CD and MLOps processes, and ensuring secure integration of internal data sources and APIs.

Qualifications

• Several years of experience as AI, ML, Data, or Cloud Engineer or similar technical role
• Experience in LLM development, ideally with RAG, embeddings, vector search, tool calling, and agentic AI
• Strong Python and SQL skills; experience with Git, automated testing, code reviews, and modular development
• Solid experience with Databricks, including Spark, Delta Lake, workflows, Unity Catalog, MLflow, and Mosaic AI
• Good knowledge of Azure, especially OpenAI, Managed Identities, Key Vault, Storage, Networking, and Monitoring
• Experience with productive integration of APIs, data sources, and enterprise systems; distributed/event-based architecture
• Experience in CI/CD, MLOps, and Infrastructure as Code, e.g., GitHub Actions/Azure DevOps, Terraform/Bicep
• Strong understanding of Cloud Security, data protection, and AI governance
• Monitoring of productive AI solutions; systematic evaluation of prompt, retrieval, and agent quality
• Ability to translate business requirements into robust, maintainable, and scalable technical solutions
• Interdisciplinary collaboration, self-responsibility, and clear communication of complex technology

Responsibilities

• Design and develop productive AI solutions on Databricks and Azure from prototype to scalable operation
• Develop AI agents and multi-agent workflows integrating Large Language Models, enterprise data, APIs, and internal systems
• Implement RAG, Tool-Calling, and workflow architectures; develop reusable components for prompts, tools, agents, and data access
• Integrate Azure OpenAI and other foundation models into Databricks and existing application landscapes
• Develop data and processing pipelines on Databricks using Python, SQL, Spark, and Delta Lake
• Utilize Databricks Mosaic AI and MLflow for model development, versioning, evaluation, deployment, and monitoring
• Automate data and AI-driven processes with Databricks Workflows and integrate into existing enterprise processes
• Design Azure and Databricks architecture for secure, scalable, and cost-efficient operations
• Establish CI/CD and MLOps processes for data pipelines, models, and agents, including automated testing and deployment
• Develop quality and evaluation procedures for AI applications, focusing on response quality, groundedness, security, latency, cost, and reliability
• Ensure secure and traceable operations through Unity Catalog, Secrets Management, Managed Identities, RBAC, and monitoring
• Securely connect internal data sources, systems, and APIs; support departments in implementing AI and automation use cases
• Collaborate closely with Product Owners, departments, Data and Cloud teams, and Security; contribute technical perspective to architecture decisions
• Document architectures, data flows, agent logic, operational processes, and security controls for sustainable handover to production

Skills

Contract length

Relocation