Responsibilities
- Designing and delivering AI-enabled services that support IFOM’s scientific activities and internal operations, including LLM applications, RAG systems, agent-assisted workflows, document and knowledge workflows, NLP pipelines, and other tools that can be operated as shared services.
- Working with colleagues across Research Computing & Data Science, IT/ICT, and scientific units to understand needs, prototype solutions quickly, and evolve promising ideas into reliable services.
- Applying software engineering discipline to AI work: version-controlled code, prompts, data, and models; reproducible environments; automated workflows; clear documentation.
- Evaluating open-source and commercial models and services, including managed APIs and biomedical/clinical models (e.g., BioBERT, PubMedBERT, MedGemma, Meditron, and similar).
- Contributing to the deployment and operation of AI services across on-premises infrastructure, cloud environments, managed AI services and APIs, and academic computing resources.
- Helping adopt good engineering and MLOps practices for evaluation, monitoring, versioning, and maintainability.
- Working closely with the Research HPC Engineer and the Bioinformatics Engineer.
- Interfacing effectively with the ICT department on infrastructure, security, networking, and integration topics.
- Producing clear technical documentation and contributing to internal training.
- Strong proficiency in Python, including familiarity with the ML ecosystem (e.g., PyTorch, Hugging Face).
- Solid fundamentals in machine learning and deep learning.
- Hands-on experience with LLMs and modern AI tooling such as RAG, prompt engineering, vector databases, LangChain, or LlamaIndex.
- Comfort working in Linux environments, using Git, and building reproducible workflows.
- Containerization experience with Docker and Apptainer (Singularity).
- Working knowledge of Kubernetes.
- Interest in cloud-based AI services and APIs.
Preferred qualifications
- Exposure to managed AI services and cloud-based AI APIs.
- Familiarity with open-source or self-hosted model serving solutions.
- Experience with HPC environments and workload schedulers such as SLURM.
- Exposure to MLOps tools for experiment tracking, evaluation, CI/CD, or model lifecycle management.
- Familiarity with Infrastructure-as-Code and deployment automation.
- Experience with fine-tuning, quantization, evaluation methodologies, or agentic workflows.
- Background in biomedical or life sciences domains.
- Advanced proficiency in spoken and written English.
- Learning and teamwork attitude.
- Problem solving.
- Good interpersonal skills and empathy.
- Curiosity and learning agility.
- Engineering rigor and service mindset.
- Ownership and reliability.
- Ability to collaborate across scientific, engineering, and IT/ICT functions.
- Clear written and verbal communication.
Benefits
- International work environment.
- Continuous training.
- Flextime.
- Guest house inside the campus.
- Discount agreements for gym, pharmacy, psychology services, and local shops.
- Bar and canteen inside the campus with lunch benefit.
- Company nursery.
- Relocation and family support through the Welcome Office.
- Medical service.
- Laptop.
- CAF service for tax declaration.
- Lab G: a laboratory specifically designed for pregnant or breastfeeding women researchers.
Tags & focus areas
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