Job Specifications
Syensqo is all about chemistry. We’re not just referring to chemical reactions here, but also to the magic that occurs when the brightest minds get to work together. This is where our true strength lies. In you. In your future colleagues and in all your differences. And of course, in your ideas to improve lives while preserving our planet’s beauty for the generations to come.
Syensqo.ai - AI Engineer
Syensqo is a leading multinational materials company headquartered in Brussels, advancing humanity through scientific exploration and innovation. Nearly two years after the spin-off from Solvay, Syensqo.ai has evolved from inception to a nimble, high-impact scale-up that accelerates AI adoption across the Group—partnering closely with corporate functions (e.g., Finance, HR, Legal, Procurement), operations, and our chemists and researchers.
We operate lean and fast, with a startup mindset and strong governance: deliver impact quickly, build responsibly, and meet enterprise standards for security, compliance, ethics, and guardrails. As an AI Engineer, you will design, build, and productionize agentic and generative AI solutions on Azure, with a focus on real business outcomes, reproducibility, and maintainability (MLOps).
The AI Engineer will co-lead the planning, execution, and delivery of AI proofs-of-value and products, ensuring they solve concrete business problems and can scale across Syensqo. You will collaborate directly with stakeholders, guide technical decisions, and help shape patterns and standards for agentic AI within the enterprise.
Building on this context, the AI Engineer will focus on HR and executive productivity: designing GPTs, copilots, and automations that accelerate recruiting, onboarding, knowledge retrieval, policy guidance, and decision support. You will translate ambiguous problems into robust, measurable solutions that integrate with Microsoft 365 and enterprise HR systems.
You are independent, solution-oriented, and passionate about automating workflows with AI—bringing a track record of hands-on delivery (including side projects) and comfort across Azure AI Foundry, Copilot Studio, and Azure AI Document Intelligence. The position reports to the AI Lead.
Key Responsibilities:
Build HR-facing GPTs, copilots, and automations using Azure AI Foundry and Copilot Studio;
Design RAG assistants for HR knowledge, policies, and documents using Azure AI Search and Document Intelligence;
Partner with executive staff to craft bespoke agents and productivity workflows integrated with Microsoft 365;
Frame problems with HR/executive stakeholders; define success metrics; deliver measurable value quickly;
Implement evaluation: quality, safety, latency, cost; run A/B tests; iterate from telemetry;
Enforce Responsible AI guardrails, privacy, and approvals; collaborate with Security, Legal, and Compliance;
Integrate assistants with enterprise data and HR systems via secure APIs, connectors, and Microsoft Graph;
Establish CI/CD for prompts, workflows, and models with Azure DevOps; versioning, rollback, canary releases;
Monitor production for drift, safety incidents, and cost; optimize usage, quotas, and caching;
Document patterns, templates, and runbooks; publish reusable components for the community;
Enable power users and citizen developers through workshops, office hours, and concise guidance;
Contribute to internal standards for agentic AI, MLOps, and governance across Syensqo.
Education and experience:
Education: Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Mathematics, or related field — or equivalent practical experience. Background in Chemistry/Cheminformatics is a plus.
Experience:
Solid hands-on experience delivering AI/GenAI solutions (tenure flexible; we value demonstrated capability over years);
Proven track record building GPT-based assistants, RAG pipelines, and workflow automations for corporate and executive use cases;
Experience working with corporate business functions (especially HR) and senior leaders; excellent stakeholder management and service mindset;
Demonstrated MLOps implementation on Microsoft Azure;
Experience of Copilot Studio is a plus;
Open-source contributions, internal frameworks, or published case studies are a strong plus.
Technical Knowledge:
Good prompting expertise;
Python expertise; proficiency with ML/LLM stacks (PyTorch; scikit-learn; Transformers); prompt engineering and evaluation;
LLM/agent frameworks such as LangChain, LlamaIndex, or Semantic Kernel;
Azure services: Azure Machine Learning, Azure OpenAI / Azure AI Foundry, Azure AI Search, Azure Data Factory, Azure DevOps, Microsoft Fabric; containers (Docker; AKS/Container Apps);
Microsoft ecosystem: Copilot Studio, Power Automate/Power Platform, Microsoft 365 Graph, Azure AI Document Intelligence; secure API integration and data governance;
Data engineering fundamentals: SQL/NoSQL, ETL/ELT, events/queues, and secure cloud data integration.
Skills and behavioral competencies:
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