Podstawy

Senior Data Scientist / ML Engineer

murcia, Spain Firma: JR Spain Klient / Pracodawca: Intellias
Opublikowano: 28.05.2026
Data zamknięcia: 12.07.2026
Referencje dotyczące stanowiska: 347549944801499545632460

Informacje o stanowisku

Lokalizacja
murcia, Spain
Firma
JR Spain
Klient / Pracodawca
Intellias
Referencje dotyczące stanowiska
347549944801499545632460
Typ wpisu
Podstawy
Wymagane pozwolenie na pracę UE
Nie
Opublikowano
28.05.2026
Data zamknięcia
12.07.2026

Opis stanowiska

Senior Data Scientist / ML Engineer

Location: Remote from Spain (an indefinite Spanish employment contract)

Working hours: readiness to work till 2-3 PM EST hours (8-9 PM CET)


Are you a skilled Machine Learning engineer with a passion for Computer vision, NLP or Generative AI? Do you have a knack for understanding both the technical intricacies and the business implications of data-driven solutions? If so, we have an exciting opportunity for you to join our team as Machine Learning Engineer.


Requirements:


Education: Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related quantitative field.

Experience: 5+ years of professional experience in machine learning engineering, AI development, or a closely related role.

Machine Learning & Statistics: Solid understanding of classical ML algorithms (e.g., tree-based models, SVMs, clustering, ensemble methods), feature engineering, model evaluation metrics, and statistical methods (hypothesis testing, regression analysis, probability distributions).

LLM Expertise: Demonstrated project experience with large language models, including:

  • Prompt engineering and prompt management strategies;
  • LLM application development (end-to-end);
  • Fine-tuning of large language models;
  • Retrieval-augmented generation (RAG) pipeline design and implementation;
  • Practical experience with vector stores such as ChromaDB, pgvector, and PostgreSQL.

AI Agents: Hands-on experience building AI agents and multi-agent systems using frameworks such as LangChain, LangGraph, CrewAI, or similar orchestration frameworks. Must demonstrate the ability to design agent architectures, manage tool integration, and handle complex agent workflows.

Programming: Proficiency in Python with a strong emphasis on writing clean, maintainable, production-quality code. Familiarity with software engineering best practices (testing, code review, documentation).

Cloud: Practical experience with Google Cloud Platform (GCP) services for ML workloads (e.g., Vertex AI, Cloud Run, GCS, BigQuery, Compute Engine).

DevOps & MLOps:

Docker: Proficiency in containerization — building, managing, and deploying Docker images and containers.

GitLab: Proficient GitLab skills for version control, merge request workflows, and repository management.

API Development: Experience with FastAPI, including request validation, async handling, and integration with ML model serving. Broader software development experience expected.

At least B2 level of English.


Soft Skills:


  • Excellent communication skills
  • Strong work ethic and high personal accountability
  • Ownership mentality — takes full responsibility for deliverables and outcomes
  • Proactive, self-starting approach to identifying problems and driving project success without waiting for direction.


Responsibilities:


  • Drive/Participate the ideation, development, and execution of POCs and AI related project
  • Develop and implement machine learning models, algorithms, and data-driven solutions to address complex business problems
  • Collaborate cross-functionally with engineering, product management, and other relevant teams to integrate data-driven functionalities into our products

Umiejętności

apply blended learning apply for research funding apply research ethics and scientific integrity principles in research activities build recommender systems Business Analytics Business Intelligence collect ICT data communicate with a non-scientific audience Computational Biology Computer Simulation conduct research across disciplines create data models Data Engineering data ethics Data Mining Data Models data quality assessment Data Science data visualisation software define data quality criteria deliver visual presentation of data demonstrate disciplinary expertise design database in the cloud design database scheme develop data processing applications develop professional network with researchers and scientists Digital Curation disseminate results to the scientific community draft scientific or academic papers and technical documentation empirical analysis establish data processes evaluate research activities execute analytical mathematical calculations Hadoop handle data samples Healthcare Analytics image recognition implement data quality processes increase the impact of science on policy and society information categorisation Information Extraction integrate gender dimension in research integrate ICT data interact professionally in research and professional environments interpret current data LDAP LINQ make data-driven decisions manage data manage data collection systems manage findable accessible interoperable and reusable data manage ICT data architecture manage ICT data classification manage intellectual property rights manage open publications manage personal professional development manage research data Marketing Analytics mathematical modelling MDX mentor individuals multidisciplinary research N1QL normalise data online analytical processing operate open source software perform data cleansing perform data mining perform project management perform scientific research promote open innovation in research promote the participation of citizens in scientific and research activities promote the transfer of knowledge publish academic research quantitative analysis query languages report analysis results Research Design resource description framework query language Scientific Computing scientific literature Social Network Analysis SPARQL speak different languages State Estimation statistical modeling techniques Statistics synthesise information teach in academic or vocational contexts think abstractly Unstructured Data use data processing techniques use databases use spreadsheets software visual presentation techniques write scientific publications XQuery

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