AI Azure Foundry Engineer

🏝️ Comprehensive training and development programs.

🏝️ Flexible working schedule

🏝️ Private medical insurance 100% covered by SD Worx

🏝️ 25 vacation days

🏝️ 18 hours for personal matters

🏝️ Reduced summer hours: 36 hours instead of 40 per week, for 4 weeks (to be chosen in July or August)

🏝️ Edenred flexible retribution options: meal vouchers, transport vouchers, etc.

🏝️ Laptop and equipment provided

🏝️ Workation: possibility to work from any SD Worx location (4 weeks per year, EU only)

🏝️ Learning opportunities: individual training bonus, an individual development plan, and professional training

🏝️ Career growth: whether you want to become more of an expert in your field or want to expand your knowledge more horizontally, there is always room to grow within SD Worx!

Salary range information will be shared with candidates during the first call with the Talent Acquisition team.

We are looking for an AI / Azure Foundry Engineer to accelerate our organisation's transition into AI-enabled smart automation. You will use Azure AI Foundry, Python, and advanced AI tooling to design intelligent automation solutions that eliminate repetitive work and improve process efficiency for our internal teams - with special focus on Payroll and Products.

Beyond project delivery, you will help shape the engineering direction, reusable patterns, and technical standards for all AI-driven automation within the Smart Automation team.

• Design, build, and deploy intelligent automation solutions leveraging the Microsoft Azure ecosystem, including Azure AI Foundry, Azure OpenAI, Azure AI Search, model orchestration, vector search, secure prompt pipelines, and evaluation frameworks.

• Develop and maintain production-grade Python services integrated with AI models, Azure AI Foundry workflows, AI hubs, datasets, APIs, and enterprise systems.

• Create reusable AI components, frameworks, and engineering patterns that can be adopted across multiple teams and business functions.

• Define and champion best practices for prompt engineering, model governance, AI evaluation, security, and responsible AI deployment.

• Contribute to AI architecture discussions, technical design decisions, performance optimization, and engineering standards across the Smart Automation team.

• Design and deliver end-to-end AI-powered automation solutions addressing high-volume, repetitive business processes across Payroll, Products, and other internal functions.

• Build and implement solutions such as:

  • Intelligent document processing and document understanding

  • Natural Language Processing (NLP) workflows

  • AI assistants and agentic automation solutions

  • Retrieval-Augmented Generation (RAG) and Retrieval-Augmented Automation (RAA) solutions

  • Knowledge management and enterprise search solutions

• Develop secure integrations between AI services and enterprise applications using APIs, microservices, and Python-based automation.

• Build and maintain pipelines for data ingestion, transformation, embeddings generation, vector indexing, retrieval, monitoring, and evaluation.

• Partner closely with Payroll, Product, and Business SMEs to identify automation opportunities and drive measurable business outcomes.

• Translate business problems into scalable AI solution designs and technical requirements.

• Clearly communicate AI capabilities, limitations, risks, and value to both technical and non-technical stakeholders.

• Collaborate with engineers across the Smart Automation team to ensure consistent architecture, engineering quality, and reusable design patterns.

• Ensure all solutions follow modern engineering practices including version control, testing, observability, monitoring, and secure software development.

• Participate in incident reviews and continuously improve AI architectures through lessons learned.

• Promote best practices for model evaluation, telemetry, traceability, prompt management, and responsible AI.

• Stay current with emerging Azure AI and Generative AI capabilities and proactively identify safe adoption opportunities.

• All code must be committed through feature branches with peer-reviewed pull requests.

• Unit tests should be developed alongside code and all solutions must include observability and monitoring from day one.

• Secrets and credentials must be stored in Azure Key Vault and never in source code repositories or pipeline variables.

• Azure service-to-service communication must use Managed Identity and Zero Trust principles wherever possible.

• All AI solutions must implement Content Safety controls, grounding mechanisms, evaluation frameworks, and Responsible AI guardrails before deployment to staging or production environments.

• Engage proactively with Payroll and Product SMEs to validate business requirements and AI solution designs before development begins.

• Communicate AI capabilities, limitations, risks, and confidence levels transparently, especially with non-technical stakeholders.

• Share reusable components, technical learnings, and best practices across the Smart Automation team.

• Raise risks, dependencies, and blockers early to ensure timely delivery and maintain sprint commitments.
  
  What do we have to offer?