Recruitment process
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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 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 a Senior AI Engineer, you are a technical anchor and team lead within Syensqo.ai. In addition to designing and productionizing high-impact AI solutions, you will lead, coach, and structure the work of a growing team of AI Engineers, many of whom will be early-career graduates.
You operate at the intersection of deep technical expertise, delivery leadership, and people development. You set technical direction, ensure execution quality, and actively grow the capabilities and autonomy of the team, while remaining hands-on on the most complex problems.
You will play a key role in scaling Syensqo.ai sustainably: establishing standards, accelerating onboarding, and ensuring that junior engineers quickly reach productivity while adhering to enterprise-grade engineering and Responsible AI practices.
The position reports to the Data Science Lead.
Key Responsibilities:
Lead the end-to-end design, implementation, and deployment of agentic and generative AI solutions (tool-using LLMs, function calling, multi-step workflows) that integrate with enterprise systems and data.
Architect RAG pipelines (retrieval, indexing, chunking, routing, safety filters, evaluation) using Azure services (e.g., Azure AI Search, Azure OpenAI / Azure AI Foundry, Azure Machine Learning) and modern frameworks.
Own fine-tuning and adaptation (e.g., LoRA/PEFT, instruction tuning, prompt optimization) and establish offline/online evaluation strategies (hallucination, safety, latency, cost).
Drive MLOps on Azure: CI/CD for prompts and models, model/data/versioning, telemetry, guardrails, rollback strategies, canary releases, monitoring, and cost governance.
Serve as a technical authority on Azure (Azure ML, Azure DevOps, Azure Functions, Microsoft Fabric, Azure AI/OpenAI, Azure AI Search; familiarity with AKS/Container Apps is a plus).
Provide hands-on coaching, technical guidance, and structured feedback through code reviews, design sessions, and pair working.
Mentor engineers/scientists, share best practices, and contribute reusable components, templates, and reference architectures.
Set the standards and culture of exploration for a new local office.
(Optional, valued) Contribute to initiatives involving chemistry/materials datasets and domain-specific tooling.
Education and experience
- Education: Master’s or PhD 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 production AI/GenAI solutions (tenure flexible; we value demonstrated capability over years).
Proven track record building agentic LLM systems (tool use/function calling, orchestration, safety), RAG pipelines, and fine-tuning/adaptation.
Experience leading a team / startup - a track record of entrepreneur mindset
Demonstrated MLOps implementation on Microsoft Azure.
Open-source contributions, internal frameworks, or published case studies are a strong plus.
Technical Knowledge:
Python expertise; proficiency with ML/LLM stacks (PyTorch; scikit-learn; Transformers).
LLM/agent frameworks such as LangChain, LlamaIndex, Semantic Kernel (or equivalents).
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).
Data engineering fundamentals: SQL/NoSQL, ETL/ELT, APIs/eventing, and secure cloud data integration.
Familiarity with chemistry/cheminformatics data and tools is a plus.
Skills and behavioral competencies
Startup mindset & ownership: proactive, resourceful, bias to action, comfortable with ambiguity, drives ideas to value.
Technical leadership: sets architecture, makes pragmatic trade-offs (quality, cost, latency), mentors peers.
Technical leadership & role modeling: sets direction, leads by example, earns trust through competence.
Curiosity & craftsmanship: keeps pace with GenAI/agentic advances; evaluates and integrates what truly works.
Communication & evangelism: explains complex topics clearly to non-technical audiences; runs workshops/demos.
Collaboration & knowledge sharing: reusable components, patterns, docs; uplifts team capabilities.
Responsible AI: builds with security, privacy, and compliance by design.
Language skills
- English (mandatory)
- French (nice to have)
What’s in it for the candidate
- Be part of — and contribute to — a once-in-a-lifetime transformation at Syensqo: small, empowered team; direct line of sight to impact; modern Azure stack; autonomy to ship meaningful AI products used across the Group.
Additional information
- Location: Morocco, Ben Guerir
- Remote work: partial possible
- Level: S18-19
- Contrat type : CDI
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