Senior System Specialist

Coopers Group; Basel; Vor Ort; CHF 120.000 - 210.000; Vollzeit

Zusammenfassung

Coopers Group is seeking an experienced AI/ML software engineer to design and deploy agentic workflows for clinical data management, using frameworks like LangGraph, CrewAI, and AutoGen. You will implement HITL processes, CI/CD pipelines, and scalable cloud-based data solutions in AWS.

The role requires deep Python expertise, strong data engineering experience, and a track record delivering AI/ML solutions in regulated environments. Collaboration with quality and governance teams is essential.

Qualifikationen

• 5+ years’ experience with CS/ML/engineering degree; 8+ years delivering AI/ML solutions and 3+ years in full-stack data engineering.
• Deep proficiency in Python and hands-on experience with AI orchestration frameworks like LangGraph, CrewAI, AutoGen, or LlamaIndex.
• Solid experience with modern data engineering (SQL/NoSQL, APIs), vector databases, and optimizing RAG pipelines.
• Strong experience deploying AI systems on AWS, containerization (Docker, Kubernetes), CI/CD pipelines, Git, and observability.
• Strong understanding of model reproducibility, interpretability, and validation in highly regulated environments.
• Excellent problem-solving and ability to explain complex concepts to diverse stakeholders.

Aufgaben

• Design and deploy sophisticated agentic workflows using LangGraph, CrewAI, or AutoGen to automate complex clinical data management tasks.
• Build collaboration systems where specialised agents resolve end-to-end data challenges through testable code and CI/CD pipelines.
• Apply systematic prompt engineering techniques (Chain-of-Thought, ReAct, Reflection) to ensure high-fidelity outputs in clinical contexts.
• Deploy AI agents into our cloud environment (AWS) while building scalable applications that capture, move, and prepare scientific data.
• Partner with Data Integrity and Quality teams to ensure agentic decisions are traceable and compliant with regulatory standards.
• Create reliable HITL workflows and tool-calling agents to solve highly regulated data challenges.

Kenntnisse

Python

AI orchestration

Full-stack software

Data engineering

Ausbildung

Bachelor’s degree in CS/ML/Engineering or Biomedical Informatics

Master’s/PhD in CS/AI or related field

Tools

LangGraph

CrewAI

AutoGen

LlamaIndex

SQL

NoSQL

APIs

Docker

Kubernetes

Jobbeschreibung

Tasks & Responsibilities:

• Design and deploy sophisticated agentic workflows using frameworks such as LangGraph, CrewAI, or AutoGen to automate complex clinical data management tasks.
• Build collaboration systems where specialised agents (e.g., Planner, Validator, and Protocol Reviewer) resolve end-to-end data challenges through high-quality, testable code and CI/CD pipelines.
• Apply systematic prompt engineering techniques (including Chain-of-Thought, ReAct, and Reflection) to guarantee high-fidelity outputs in a clinical context.
• Deploy AI agents into our cloud environment (AWS) while building and enhancing scalable applications that capture, move, and prepare scientific data.
• Partner with Data Integrity and Quality teams to ensure all agentic decisions are traceable, reproducible, and fully compliant with regulatory standards.
• Create reliable "Human-in-the-loop" (HITL) workflows and tool-calling agents to solve complex, highly regulated data challenges.

Must Haves:

• 5+ years’ experience and a Bachelor’s or advanced degree in Computer Science, Machine Learning, Engineering, or Biomedical Informatics, alongside 8+ years of experience delivering AI/ML solutions plus 3+ years in full-stack software and data engineering
• Deep proficiency in Python and hands-on experience with AI orchestration frameworks like LangGraph, CrewAI, AutoGen, or LlamaIndex
• Solid experience with modern data engineering (SQL/NoSQL, APIs), Vector Databases (e.g., Chroma, Pinecone), and optimizing RAG pipelines
• Strong experience deploying AI systems on AWS, containerization (Docker, Kubernetes), CI/CD pipelines, Git, and service observability (logging, tracing, metrics)
• A strong understanding of model reproducibility, interpretability, and validation best practices within highly regulated environments
• Excellent problem-solving capabilities with the ability to explain complex technical concepts clearly to diverse global stakeholders

Nice to Haves:

• Master’s or PhD in Computer Science, AI, or a related quantitative field
• Experience in a highly regulated industry such as Healthcare, FinTech, or Aerospace
• Understanding / Showcasing practical, end-to-end implementation of security frameworks in complex agentic workflows
• Understanding of clinical trial life cycles and data standards
• Familiarity with monitoring tools for system performance, workflow analytics, and MLOps/DevOps best practices
• Contributions to open-source agentic frameworks or internal AI tooling in the automation or productivity space
• Experience to architect scalable solutions in complex environments with multiple existing system integrations
• Experience with other programming languages (e.g., C#, Java, JavaScript, R, RShiny) is considered an advantage
• Sharing your GitHub account, showcasing relevant work, is considered an advantage