LLM Engineer
DocPlanner
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇪🇸 Spain
Visit company websiteJob Level
Junior
Tech Stack
Python
About the role
- Join the global Machine Learning and Data Science unit and embed in the Noa product line used by doctors across multiple countries
- Support a product area within Noa to deliver end-to-end LLM capabilities
- Work alongside machine learning scientists, engineers, and country-specific linguists; report to the Head of Machine Learning & Data Science
- Design, deploy and iterate over LLM services for text-based applications and beyond
- Build small to medium-sized Python projects and collaborate on production code and deployments at scale
- Assess platform engineering and LLMOps bottlenecks; research and design scalable prompt management strategies
- Research, architect, and deploy LLM-powered information retrieval solutions (e.g., RAG) for complex multilingual environments
- Partner with the AI Platform team to refine LLMOps best practices, evolve frameworks, and establish scalable workflows
- Deliver initiatives that directly contribute to business objectives and iterate from prototype to production
Requirements
- At least one year of professional experience in LLM development or integration in a fast-paced, product-driven tech environment
- Demonstrated expertise in production-grade LLM deployments, including prompt management systems, vector databases, semantic search implementation, and API integration with foundation models
- Good understanding of transformer architectures
- Proficiency in LLM frameworks such as LangChain, LlamaIndex, or similar tools
- Proficiency in Python
- Experience in collaborative project development and good engineering practices
- Proven experience in evaluating LLMs through systematic testing, benchmark design, and development of custom metrics (accuracy, consistency, factuality, bias)
- Proven ability to integrate, deploy, and optimize large language models in production-grade environments ensuring scalability and robust performance
- Strong knowledge in prompt engineering, agent-based workflows, and generation/manipulation of embeddings
- Experience with RAG (Retrieval-Augmented Generation) techniques, vector similarity search, and information retrieval methods
- Problem-solving mindset, adaptability, and ability to manage timelines and deliver under tight deadlines
- Curiosity and eagerness to collaborate with cross-functional teams
Benefits
- A salary adequate to your experience and skills.
- Flexible remuneration and benefits system via Flexoh (restaurant card, transportation card, kindergarten, and training tax savings)
- Share options plan after 6 months of working with us
- Remote or hybrid work model with our hub in Barcelona
- Flexible working hours
- Summer intensive schedule during July and August (work 7 hours, finish earlier)
- 23 paid holidays, with exchangeable local bank holidays
- Additional paid holiday on your birthday or work anniversary
- Private healthcare plan with Adeslas for you and subsidized for your family (medical and dental)
- Access to hundreds of gyms for a symbolic fee in partnership with Wellhub for you and your family
- Access to iFeel, a technological platform for mental wellness offering online psychological support and counseling
- Free English classes
- Equal opportunities in hiring and adaptable recruitment process
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
LLM developmentproduction-grade LLM deploymentsprompt management systemsvector databasessemantic search implementationAPI integrationtransformer architecturesLLM frameworksPythonprompt engineering
Soft skills
problem-solvingadaptabilitytime managementcollaborationcuriosity