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Senior Data Engineer, Applied Machine Learning
HíbridoTiempo completoIngeniería de datosEnterprise SoftwareData Analytics
Build production data and machine-learning pipelines that classify, normalize, structure, and match product data across a catalog of more than a billion records. Work with LLMs and other ML methods, scale solutions on cloud infrastructure, and collaborate with Product and R&D teams.
Responsabilidades
- Design and build LLM- and ML-powered pipelines for classifying, normalizing, structuring, and matching product, brand, and category data
- Build agentic workflows to automate complex data tasks end to end
- Select and evaluate LLMs, embeddings, fine-tuned models, classifiers, and rules while balancing accuracy, cost, and latency
- Scale data processing across billions of records using Spark, Databricks, and cloud infrastructure
- Create evaluation frameworks, ground-truth datasets, labeling processes, quality metrics, and ongoing monitoring
- Take solutions from proof of concept through production and own them after launch
- Work with Product to define requirements and shape the roadmap
- Collaborate with data engineers, data scientists, and R&D teams on infrastructure and best practices
Requisitos
- BSc or MSc in Computer Science, Data Science, Mathematics, or a relevant field
- At least 4 years of hands-on experience as a data engineer, ML engineer, or data scientist, with production solutions
- Strong Python skills and ability to write production-quality code
- Production experience building LLM applications, including prompt engineering, structured outputs, RAG, embeddings, and evaluation
- Experience with LLM provider APIs, LangGraph or LangChain, Hugging Face, and vector stores
- Knowledge of classical and modern text classification and NLP methods
- Experience processing large-scale data with Spark or PySpark, Databricks or similar, and AWS or another cloud
- Understanding of model evaluation and data quality, including precision/recall trade-offs, ground-truth creation, and error analysis
Se valora
- Experience with taxonomies, entity resolution, or product and e-commerce data
- Experience fine-tuning or deploying open-source models
Beneficios
- Competitive compensation and benefits
- Regular team outings and social events
- Career development resources, coaching, and learning opportunities
- Flexible work arrangement with partial work from home