DATA SCIENCE SPECIALIST
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Tech stack
Job description
Collaborate with infrastructure and DevOps teams to deploy secure and scalable cloud and on-premise environments (including GPU partitioning), optimizing performance, resources, and costs. Partner with AI Engineers and cross-functional teams to transform prototypes into robust, production-grade AI solutions. Monitor and optimize the performance of AI applications, implementing observability best practices (logging, metrics, alerting) to detect incidents, data drift, and model deviations in production environments. Stay up to date with the latest developments in AI frameworks, cloud technologies, and software engineering practices, applying relevant improvements to production systems. Beneficios corporativos y otra información: Summer and Christmas intensive workday, as well as every Friday throughout the year. Working hours: Monday to Friday with flexible entry hours. Possibility to participate in the company corporate benefits: Private health insurance, restaurant vouchers, opportunity to
Requirements
teach English classes... Discounts on major brands: Textiles, consumer goods, leisure, electronics, travel agencies... Ongoing and specialized training provided by the organization. Position located in Malaga Contract: Permanent. Remote work: Possibility of telecommuting through a hybrid system after a successful trial period. Qualifications: ~ Bachelor's degree in Computer Science, Engineering, or another STEM field. ~4+ years of experience in AI, Data Science, or ML Engineering applied to real-world environments. ~ Strong programming skills in Python and solid understanding of software engineering principles (testing, clean code, CI/CD). ~ Experience developing and deploying AI or ML solutions in Azure (e.g., Azure Machine Learning or Azure AI Services). ~ Proficiency with PyTorch, FastAPI, and data/ML libraries such as Pandas or Scikit-learn. ~ Experience with MLOps tools such as MLflow, Airflow, or Dagster. Other desirable knowledge: Experience with LLM frameworks (LangChain, LlamaIndex) and agent-based automation (CrewAI, Agno, MCP, etc.). Knowledge of cloud-native infrastructure (Docker, Kubernetes, GPU partitioning). Familiarity with observability and model monitoring tools (EvidentlyAI, InspectAI, DeepEval). Experience with at least one vector databases (Milvus, Qdrant, ChromaDB, Pinecone, ElasticSearch). Familiarity with distributed data processing (Dask, Polars) and LLM optimization (LoRA, QLoRA). Hands-on experience with CI/CD pipelines (GitHub Actions) and DevOps practices.