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.
Assess and understand local specificities on the AI market - spot opportunities and potential partnerships with local AI leaders.
Working close to local leadership to provide AI solutions.
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).
Work side-by-side with business stakeholders in corporate functions and R&I to frame use cases, define success metrics, and deliver measurable value.
Champion Responsible AI: privacy, security, compliance, ethical use, and documentation across the lifecycle.
Mentor engineers/scientists, share best practices, and contribute reusable components, templates, and reference architectures.
Promote adoption through demos, workshops, and concise internal write-ups; stay current on GenAI/agentic research and bring relevant advances into production.
(Optional, valued) Contribute to initiatives involving chemistry/materials datasets and domain-specific tooling.
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.
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 working with corporate business functions (e.g., Finance, HR, Legal, Procurement) and/or R&I stakeholders.
Demonstrated MLOps implementation on Microsoft Azure.
Open-source contributions, internal frameworks, or published case studies are a strong plus.
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.
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.
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.
Business impact orientation: frames problems with stakeholders, defines KPIs, measures value, iterates quickly.
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.
