2 дня назад
Data Engineer – Applied ML
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Engineer – Applied ML (LLMs/NLP): Building production ML and LLM pipelines that classify, normalize, structure, and match retail product data across a catalog of more than a billion records with an accent on taxonomy, entity resolution, and scalable data processing. Focus on designing agentic workflows, evaluating model quality, balancing accuracy, cost, and latency, and taking solutions from proof of concept to production.
Location: Tel Aviv, Israel; hybrid work with partial work-from-home availability
Company
provides a digital intelligence platform used by more than 3,500 global customers, including major brands and retailers.
What you will do
- Design and build LLM-powered and ML-based pipelines for classifying, normalizing, structuring, and matching product, brand, and category data.
- Build agentic workflows with LangGraph or similar frameworks to automate complex data tasks.
- Select and combine LLMs, embeddings, fine-tuned models, classical classifiers, and rules while balancing accuracy, cost, and latency.
- Scale data-processing solutions across catalogs containing more than a billion records using Spark, Databricks, and cloud infrastructure.
- Build evaluation frameworks with ground-truth datasets, labeling processes, quality metrics, error analysis, and monitoring.
- Take solutions from proof of concept to production, own them after launch, and collaborate with Product, data engineering, data science, and R&D teams.
Requirements
- B.Sc. or M.Sc. in Computer Science, Data Science, Mathematics, or a related field.
- 4+ years of hands-on experience as a data engineer, ML engineer, or data scientist with production solutions.
- Strong Python skills and experience writing production-quality code.
- Production experience with LLM applications, including prompt engineering, structured outputs, RAG, embeddings, and evaluation.
- Experience with LLM provider APIs, LangGraph or LangChain, Hugging Face, vector stores, Spark or PySpark, Databricks, and AWS or another cloud platform.
- Strong knowledge of text classification, NLP, evaluation, data quality, precision/recall trade-offs, ground-truth creation, and error analysis.
Nice to have
- Experience with taxonomies, entity resolution, or product and e-commerce data.
- Experience fine-tuning or deploying open-source models.
Culture & Benefits
- Hybrid collaboration with colleagues in the office and the ability to work partially from home.
- Competitive compensation and employee benefits.
- Regular team outings and happy hours.
- Career development through coaching, learning solutions, and internal growth opportunities.
- Inclusive workplace focused on equality, mutual respect, and diversity.
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