resume.ipynb - Python 3 (ipykernel)
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In [1]: profile.name

priya_sharma

role: "Senior Machine Learning Engineer"   experience: "7+ yrs"   loc: "City, Country"
mail: "your.email@example.com"   tel: "+00 00000 00000"   kaggle: "kaggle.com/yourname"

## Abstract

Machine learning engineer with 7+ years taking models from research to production in NLP, recommendation and forecasting. Combines solid statistics with MLOps discipline and has published at applied-ML venues.

## Feature Importance

Python / PyTorch
Statistics
NLP / LLMs
Recommender Systems
SQL / Spark
MLOps
data_mlops = [Spark, Airflow, MLflow, Feast, Docker]
methods = [NLP, RecSys, Time series, Causal inference, A/B testing]

## Experiments: Experience

Senior ML Engineer @ Quantia Analytics[Apr 2022 → Present]

Leads the personalisation ML team (5 scientists) for a 12M-user commerce platform.

  • Shipped a two-tower retrieval and ranking system that lifted click-through rate by 18% in A/B tests.
  • Fine-tuned an LLM for support triage with 91% routing accuracy, saving 14 FTE of manual sorting.
  • Built a feature store and model registry, cutting time from experiment to production from 6 weeks to 5 days.
Data Scientist @ Meridian Insights[Jun 2019 → Mar 2022]

Forecasting and experimentation for retail clients.

  • Built gradient-boosted demand forecasts for 40k SKUs, reducing stock-outs by 23%.
  • Designed an experimentation platform with CUPED variance reduction used by 9 product teams.
Data Analyst @ Northwind Research[Jul 2017 → May 2019]

Analytics and reporting for market research.

  • Automated survey analytics in Python and SQL, cutting report turnaround from 5 days to 1.

## Results: Projects

Real-time Recommendation EnginePyTorch · Faiss · Kafka

Two-tower retrieval with online re-ranking serving 3k requests/s at a p99 latency of 40 ms.

LLM Support TriageTransformers · LoRA · FastAPI

Fine-tuned a 7B model with guardrails, reaching 91% routing accuracy and running on-prem.

Paper: Sparse Demand ForecastingApplied ML Workshop 2023

Hierarchical Bayesian model that beat baselines by 12% on intermittent-demand data.

## Appendix: Education & Certs

Google Cloud Professional Machine Learning Engineer (2023)
AWS Certified Machine Learning - Specialty (2021)
DeepLearning.AI Deep Learning Specialization (2019)
M.Sc., Statistics & Machine Learning
Sample Institute of Science, 2015 - 2017