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استخدام (AI/Machine Learning Engineer (Q-Commerce در دیجی کالا

دیجی کالا

تهران (محدوده ونک)

۱ هفته پیش اطلاعات تماس

حضوری

مقطع تحصیلی اعلام نشده

سابقه دارد (۳ سال)

حقوق توافقی

آقا و خانم

تمام وقت

مشاهده اطلاعات تماس

اطلاعات بیشتر

دیجی کالا در تهران (محدوده ونک) جهت تکمیل کادر خود به افراد واجد شرایط ذیل نیازمند است.

Description

job title

About the role
We build the intelligence layer behind a fast-moving q-commerce business — search and discovery, recommendations, delivery time prediction, demand signals, and LLM-powered assistants for customers and support agents. You'll own problems end to end: framing the business question, building the model, shipping it to production, and proving it moved a metric.
What you'll work on
Build and ship ML models on real Persian-language, high-volume transactional data: ranking and semantic search, recommendation, forecasting, ETA prediction, user segmentation and profiling.
Take models to production — feature pipelines, training/inference services, monitoring, retraining. Ownership doesn't end at the notebook.
Build LLM-based systems: retrieval-augmented pipelines, agentic workflows, embedding and vector search, evaluation harnesses to prove they actually work.
Design offline and online evaluation — A/B tests, ranking metrics (nDCG, MRR, recall@k), business KPIs — and be honest about what the numbers say.
Partner with product, commercial, and operations teams to translate ambiguous goals into modeling problems.
What we're looking for
3+ years building ML or data science systems that reached real users.
Strong Python and SQL. You're comfortable in a large relational warehouse and can write queries that don't melt it.
Solid grounding in ML fundamentals — you can explain why a model works, not just call .fit.
Experience deploying something: APIs, containers, batch pipelines, orchestration. You know the difference between a model and a service.
Clear communication. You can defend a design decision to an engineer and explain the tradeoff to a business stakeholder.
Strong pluses (any of these)
Search, ranking, or recommender systems at scale.
LLM application work: RAG, agent frameworks, prompt and eval engineering, fine-tuning.
Vector databases, embedding model evaluation, ASR/speech.
ClickHouse, Spark, Airflow, or similar data infrastructure.
How to think about fit
We're hiring across a spectrum. Some of you lean toward analysis and modeling (framing problems, experimentation, statistical rigor). Some lean toward ML engineering (pipelines, serving, latency, reliability). Some lean toward AI/LLM systems (retrieval, agents, evaluation). We're not looking for someone who is all three at once — tell us where your center of gravity is and where you want to grow.

AI/Machine Learning Engineer | Q-Commerce

متقاضیان واجد شرایط می توانند با کلیک روی لینک تکمیل فرم استخدام، رزومه خود را ارسال نمایند.

اطلاعات تماس

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مهلت ۱۴۰۵/۰۹/۰۶
محل کار محدوده ونک

استخدام فرم دیجی کالا

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