Machine Learning Engineer

Build, run and maintain the ML infrastructure behind Kavak UAE's pricing: automated mapping of market listings to catalog SKUs, pricing algorithm, and other ML models that improve pricing accuracy, strategy and margin. Turn manual, person-dependent processes into reliable systems that are measured and retrained regularly. 

Key Responsibilities:

• Build and maintain the listing-to-SKU mapping engine: rules, text matching and classifiers, with confidence thresholds and human review queues.
  

• Develop an image-based car identification model (make, model, year, generation, and trim) for listings with no usable text.
  

• Manage the local pricing algorithm, monitor performance, run experiments, improve accuracy and reduce bias, and report results.
  

• Design backtests, holdout sets and audit samples to measure accuracy, and report it by segment.
  

• Productionise models as scheduled pipelines with monitoring, retraining and data quality checks.
  

• Build and maintain data pipelines from the data warehouse, scrapers, the catalog and the backoffice.
  

• Build supporting models for pricing strategy: depreciation and km curves, price elasticity, days-to-sell and demand.
  

• Turn reviewer feedback and manual overrides into training data and rules with expiry dates.
  

• Document models, assumptions and results clearly for non-technical stakeholders.
  

• Work with the Pricing, Data, Product and global teams on data access, deployment and priorities.
  

Skills & Qualifications:

• 3–5 years in ML engineering or applied data science, with at least two models taken to production.
  

• Background in e-commerce, marketplaces, automotive, fintech or any pricing/valuation domain.
  

• Experience with tabular models (pricing, regression, classification) and with text and image data.
  

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related field.
  

• A Master's in Machine Learning or Data Science is preferred.
  

Technical Skills

• Python ML stack: pandas, scikit-learn, XGBoost/LightGBM, plus PyTorch or TensorFlow.
  

• SQL and data: strong SQL on BigQuery, Databricks or a similar cloud warehouse.
  

• Pricing models: regression and quantile regression for price ranges, plus prediction intervals.
  

• Text matching and entity resolution: normalisation, fuzzy matching, tokenisation and embeddings, used to match listings to catalog SKUs.
  

• Statistics and evaluation: holdout and backtest design, calibration, confidence intervals and A/B testing.
  

• MLOps: Docker, scheduled pipelines (Airflow or similar), experiment tracking and model registry (MLflow or Weights & Biases), monitoring and retraining.
  

• Data quality: automated validation of incoming data (Great Expectations or similar).
  

• Engineering practice: Git on GitLab with CI/CD, code review and tests.
  

• Cloud: GCP or AWS.
  

• Computer vision: fine-grained image classification, transfer learning and image embeddings. Experience with vision-language models.
  

• LLMs and vector search: extracting structured fields from listing text with LLMs, and embedding matching with FAISS or a vector database.
  

• Pricing analytics: price elasticity, and survival analysis for days-to-sell.
  

• Human-in-the-loop: labelling tools (Label Studio, CVAT) and active learning to cut manual review.
  

• Model serving: REST APIs with FastAPI.
  

• Web scraping: Scrapy or Playwright, handling site layout changes and respecting each site's terms of use.
  

• Reporting: dashboards in Looker Studio, Streamlit or Databricks.
  

Nice to have

• Google Cloud Professional Machine Learning Engineer, or AWS Certified Machine
  

• Learning Engineer - Associate. (Preferred)
  

• DeepLearning.AI specialisations. (Preferred)
  

• Arabic Language