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Lead AI Engineer (Agentic AI)(m/w/x)
Designing multi-agent AI architectures and RAG pipelines for life sciences regulatory automation. Production Python engineering with agentic frameworks and LLM integration required. Employee stock ownership plan, flexible hybrid setup.
Requirements
- Technical strength, curiosity, and drive to build AI systems
- Python engineering with shipped production systems
- Hands-on with agentic frameworks (LangChain, LangGraph, Agno, or similar)
- Fluent in LLMs in production (prompting, tool use, orchestration, failure modes)
- Experience building production RAG systems (retrieval architectures, vector databases, embedding strategies, evaluation frameworks)
- Experience with agent evaluation frameworks and observability tooling (e.g. LangSmith or similar)
- Comfortable with cloud environments (AWS, GCP, or Azure) and distributed system design
- Experienced with MLOps practices (CI/CD, monitoring, deployment pipelines)
- Autonomous, low ego, and serious about work
- Excited about working in a domain with real-world consequences
- Experience in life sciences, regulated industries, or GxP-compliant environments
- Experience with multi-agent systems and workflow orchestration
Tasks
- Design and implement multi-agent architectures
- Build and maintain scalable backend services
- Own agent orchestration logic
- Design and implement RAG pipelines
- Make production-grade decisions on LLM integration
- Set engineering standards for code quality
- Drive end-to-end feature ownership
- Mentor engineers
- Shape the engineering culture
- Collaborate with product and leadership
Work Experience
- approx. 4 - 6 years
Education
- Bachelor's degreeOR
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- LangChain
- LangGraph
- Agno
- AWS
- GCP
- Azure
- LangSmith
Benefits
Competitive Pay
- Employee Stock Ownership Plan
Flexible Working
- Flexible working culture
- Hybrid setup with high trust, autonomy, and flexibility
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Lead AI Engineer (Agentic AI)(m/w/x)
Designing multi-agent AI architectures and RAG pipelines for life sciences regulatory automation. Production Python engineering with agentic frameworks and LLM integration required. Employee stock ownership plan, flexible hybrid setup.
Requirements
- Technical strength, curiosity, and drive to build AI systems
- Python engineering with shipped production systems
- Hands-on with agentic frameworks (LangChain, LangGraph, Agno, or similar)
- Fluent in LLMs in production (prompting, tool use, orchestration, failure modes)
- Experience building production RAG systems (retrieval architectures, vector databases, embedding strategies, evaluation frameworks)
- Experience with agent evaluation frameworks and observability tooling (e.g. LangSmith or similar)
- Comfortable with cloud environments (AWS, GCP, or Azure) and distributed system design
- Experienced with MLOps practices (CI/CD, monitoring, deployment pipelines)
- Autonomous, low ego, and serious about work
- Excited about working in a domain with real-world consequences
- Experience in life sciences, regulated industries, or GxP-compliant environments
- Experience with multi-agent systems and workflow orchestration
Tasks
- Design and implement multi-agent architectures
- Build and maintain scalable backend services
- Own agent orchestration logic
- Design and implement RAG pipelines
- Make production-grade decisions on LLM integration
- Set engineering standards for code quality
- Drive end-to-end feature ownership
- Mentor engineers
- Shape the engineering culture
- Collaborate with product and leadership
Work Experience
- approx. 4 - 6 years
Education
- Bachelor's degreeOR
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- LangChain
- LangGraph
- Agno
- AWS
- GCP
- Azure
- LangSmith
Benefits
Competitive Pay
- Employee Stock Ownership Plan
Flexible Working
- Flexible working culture
- Hybrid setup with high trust, autonomy, and flexibility
About the Company
Visium SA
Industry
Pharmaceuticals
Description
Visium enables enterprise executives in defining AI & Data strategy, executing large scale transformations and implementing AI across operations to become future-proof.
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