Niloofar Rahimi

Tehran, Iran | niloofar.rahimi@email.com | +98 912 000 0000 | linkedin.com/in/niloofar-rahimi | github.com/niloofar-rahimi

SUMMARY

Machine learning engineer with 5 years of experience building and operating ranking and recommendation systems at consumer scale. Owned models serving 8M+ daily sessions, cutting inference cost 35% while lifting conversion 12%. Deep production focus: model serving, A/B experimentation, and MLOps on Kubernetes.

SKILLS

• Languages: Python, SQL, Scala, Bash
• ML: PyTorch, XGBoost, scikit-learn, Transformers, learning-to-rank, recommender systems
• Data and Infrastructure: Spark, Airflow, Kafka, Docker, Kubernetes, MLflow, AWS
• Practices: A/B testing, CI/CD, model monitoring, feature stores, offline evaluation

EXPERIENCE

Senior Machine Learning Engineer — Digikala, Tehran | 2022 – Present

• Redesigned the product-ranking pipeline into a two-stage retrieval and re-ranking architecture, lifting add-to-cart rate 12% across 8M daily sessions.
• Cut inference cost 35% by distilling a transformer ranker into a gradient-boosted model that retained 98% of offline NDCG.
• Built a feature store consolidating 200+ signals, reducing new-model development time from six weeks to ten days.

Niloofar Rahimi

Tehran, Iran | niloofar.rahimi@email.com | +98 912 000 0000 | linkedin.com/in/niloofar-rahimi | github.com/niloofar-rahimi

SUMMARY

Machine learning engineer with 5 years of experience building and operating ranking and recommendation systems at consumer scale. Owned models serving 8M+ daily sessions, cutting inference cost 35% while lifting conversion 12%. Deep production focus: model serving, A/B experimentation, and MLOps on Kubernetes.

SKILLS

• Languages: Python, SQL, Scala, Bash
• ML: PyTorch, XGBoost, scikit-learn, Transformers, learning-to-rank, recommender systems
• Data and Infrastructure: Spark, Airflow, Kafka, Docker, Kubernetes, MLflow, AWS
• Practices: A/B testing, CI/CD, model monitoring, feature stores, offline evaluation

EXPERIENCE

Senior Machine Learning Engineer — Digikala, Tehran | 2022 – Present

• Redesigned the product-ranking pipeline into a two-stage retrieval and re-ranking architecture, lifting add-to-cart rate 12% across 8M daily sessions.
• Cut inference cost 35% by distilling a transformer ranker into a gradient-boosted model that retained 98% of offline NDCG.
• Built a feature store consolidating 200+ signals, reducing new-model development time from six weeks to ten days.

Niloofar Rahimi

Tehran, Iran | niloofar.rahimi@email.com | +98 912 000 0000 | linkedin.com/in/niloofar-rahimi | github.com/niloofar-rahimi

SUMMARY

Machine learning engineer with 5 years of experience building and operating ranking and recommendation systems at consumer scale. Owned models serving 8M+ daily sessions, cutting inference cost 35% while lifting conversion 12%. Deep production focus: model serving, A/B experimentation, and MLOps on Kubernetes.

SKILLS

• Languages: Python, SQL, Scala, Bash
• ML: PyTorch, XGBoost, scikit-learn, Transformers, learning-to-rank, recommender systems
• Data and Infrastructure: Spark, Airflow, Kafka, Docker, Kubernetes, MLflow, AWS
• Practices: A/B testing, CI/CD, model monitoring, feature stores, offline evaluation

EXPERIENCE

Senior Machine Learning Engineer — Digikala, Tehran | 2022 – Present

• Redesigned the product-ranking pipeline into a two-stage retrieval and re-ranking architecture, lifting add-to-cart rate 12% across 8M daily sessions.
• Cut inference cost 35% by distilling a transformer ranker into a gradient-boosted model that retained 98% of offline NDCG.
• Built a feature store consolidating 200+ signals, reducing new-model development time from six weeks to ten days.

Niloofar Rahimi

Tehran, Iran | niloofar.rahimi@email.com | +98 912 000 0000 | linkedin.com/in/niloofar-rahimi | github.com/niloofar-rahimi

SUMMARY

Machine learning engineer with 5 years of experience building and operating ranking and recommendation systems at consumer scale. Owned models serving 8M+ daily sessions, cutting inference cost 35% while lifting conversion 12%. Deep production focus: model serving, A/B experimentation, and MLOps on Kubernetes.

SKILLS

• Languages: Python, SQL, Scala, Bash
• ML: PyTorch, XGBoost, scikit-learn, Transformers, learning-to-rank, recommender systems
• Data and Infrastructure: Spark, Airflow, Kafka, Docker, Kubernetes, MLflow, AWS
• Practices: A/B testing, CI/CD, model monitoring, feature stores, offline evaluation

EXPERIENCE

Senior Machine Learning Engineer — Digikala, Tehran | 2022 – Present

• Redesigned the product-ranking pipeline into a two-stage retrieval and re-ranking architecture, lifting add-to-cart rate 12% across 8M daily sessions.
• Cut inference cost 35% by distilling a transformer ranker into a gradient-boosted model that retained 98% of offline NDCG.
• Built a feature store consolidating 200+ signals, reducing new-model development time from six weeks to ten days.

Parisa Ahmadi Tehran, Iran | parisa.ahmadi.design@email.com | parisaahmadi.design

Dear Hiring Team,

I am applying for the Senior Product Designer role on your checkout and payments team. For the past three years I have designed conversion-critical flows for one of Iran's largest e-commerce platforms, where my checkout redesign raised completion 18 percent across 4M monthly buyers — work that maps directly onto the problems your job description names.

Three things I would bring on day one. First, an experiment-driven practice: I do not ship redesigns, I ship hypotheses, and the checkout project alone ran through eleven A/B tests before full rollout. Second, design-system fluency: I built and governed a tokenized component library across web and native apps, which halved handoff time and is the reason our engineers still invite designers to architecture discussions. Third, experience designing for constraint — flaky connections, low-end Android devices, and payment methods that fail mid-flow — which I understand is increasingly relevant as you expand beyond your core markets.

I am drawn to your company specifically because you are at the scale-up stage where design decisions still move company-level metrics but a real design organization is forming; I want to help build that maturity, not just inherit it. I hold a valid passport, am ready to relocate to Berlin, and understand you support visa sponsorship.

My portfolio, including the checkout case study with full metrics, is at the link above. I would welcome the chance to walk you through it.

Kind regards, Parisa Ahmadi

Parisa Ahmadi Tehran, Iran | parisa.ahmadi.design@email.com | parisaahmadi.design

Dear Hiring Team,

I am applying for the Senior Product Designer role on your checkout and payments team. For the past three years I have designed conversion-critical flows for one of Iran's largest e-commerce platforms, where my checkout redesign raised completion 18 percent across 4M monthly buyers — work that maps directly onto the problems your job description names.

Three things I would bring on day one. First, an experiment-driven practice: I do not ship redesigns, I ship hypotheses, and the checkout project alone ran through eleven A/B tests before full rollout. Second, design-system fluency: I built and governed a tokenized component library across web and native apps, which halved handoff time and is the reason our engineers still invite designers to architecture discussions. Third, experience designing for constraint — flaky connections, low-end Android devices, and payment methods that fail mid-flow — which I understand is increasingly relevant as you expand beyond your core markets.

I am drawn to your company specifically because you are at the scale-up stage where design decisions still move company-level metrics but a real design organization is forming; I want to help build that maturity, not just inherit it. I hold a valid passport, am ready to relocate to Berlin, and understand you support visa sponsorship.

My portfolio, including the checkout case study with full metrics, is at the link above. I would welcome the chance to walk you through it.

Kind regards, Parisa Ahmadi