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# AI Engineer

- **Company:** Lifebit
- **Location:** Barcelona, Spain (Remote available)
- **Salary:** €65,000.0 - €83,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Bioinformatics, Clinical Data Repository, Software as a Service, Cloud Computing, Data Visualization, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, TypeScript, Pytorch, ReactJS, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Reliability of Systems, Scikit Learn, Kubernetes, Information Technology, Machine Learning Operations, Docker
- **Published:** July 20, 2026
- **Apply:** https://www.adzuna.es/contact-us.html

## About the Role

+ Education: BSc/MSc in Computer Science, Artificial Intelligence, Machine Learning, or a highly quantitative field (PhD preferred).
 + Experience: 2+ years of hands-on experience as an AI or ML Engineer, building a validated real product, ideally within a product-led biotech, health-tech, or SaaS company.
 + Technical Stack: Deep proficiency in Python and Typescript and standard ML frameworks (e.g., Langfuse, PyTorch, TensorFlow, JAX, Scikit-learn).
 + NLP/LLM Expertise: Proven experience working with Large Language Models, including fine-tuning, and RAG (Retrieval-Augmented Generation) architectures.
 + Cloud & Infrastructure: Familiarity with AWS/Azure/GCP and experience deploying models in Docker/Kubernetes environments.
 + Domain Knowledge: It is a plus to have experience working with biological, genomic, or clinical data is a significant advantage.
 + Autonomy: A self-starter mindset with the ability to navigate ambiguity and drive AI projects from concept to production without constant oversight.

## Description

You will own the development and deployment of machine learning models-ranging from Large Language Models (LLMs) for clinical note extraction to predictive analytics for genomic research-ensuring they are optimized for our unique federated architecture. Your work will directly empower researchers to identify drug targets and disease biomarkers faster than ever before, transforming how the world approaches precision medicine.
Your Role and Responsibilities
Agentic Architecture & Orchestration

 + Design and implement autonomous AI agents using frameworks like LangGraph, CrewAI, or AutoGen to handle complex, multi-step scientific queries.
 + Develop sophisticated reasoning loops (e.g., ReAct, Plan-and-Execute) that allow agents to decompose high-level research goals into actionable sub-tasks.
 + Build and optimize Advanced RAG (Retrieval-Augmented Generation) pipelines that integrate structured clinical data and unstructured scientific literature.
Tool-Use & Execution

 + Create and maintain "tools" for AI agents, enabling them to safely interface with Lifebit's federated APIs, SQL databases, and bioinformatic execution engines.
 + Implement secure, sandboxed code-interpreter capabilities, allowing agents to write and execute Python or R code for data visualization and statistical analysis.
 + Fine-tune LLMs for specific function-calling and tool-use accuracy within the life sciences domain.
System Reliability & Guardrails

 + Develop robust evaluation frameworks (LLM-as-a-judge) to measure agentic performance, truthfulness, and safety in a clinical context.
 + Implement "Human-in-the-loop" (HITL) patterns to ensure high-stakes scientific decisions are always reviewed by domain experts.
 + Partner with Security teams to ensure agents operate within strict data privacy boundaries, preventing prompt injection or unauthorized data egress in federated nodes.
Collaboration & Scaling

 + Work with Product and UX teams to design intuitive interfaces for interacting with agentic systems (e.g., conversational research assistants).
 + Scale agentic workloads in production using Kubernetes, ensuring low-latency reasoning and efficient token usage.


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