Machine Learning Engineer & Data Engineer Resume – Master’s Degree -LLM- RAG- Graph Databases-AWS- MLOps

Machine Learning Engineer & Data Engineer Resume – Master’s Degree -LLM- RAG- Graph Databases-AWS- MLOps

A Machine Learning Engineer and Data Engineer with a Master’s degree in Data Engineering and Analytics. Expertise in LLM Agent-Driven Tool Selection, Retrieval-Augmented Generation (RAG), Graph Databases (Neo4j), OCR, VQA, and MLOps. Experienced in building Python-integrated pipelines, Streamlit applications, and RESTful APIs with Spring Boot. Proven track record in fantasy gaming analytics, user acquisition, retention, and monetization initiatives.

Technical Skills:

Languages:

• Python, Java, R

Databases:

• MySQL, Neo4j, MongoDB, BigQuery, Redshift

DevOps and CI/CD:

• Git, Docker

Data Engineering and ETL:

• Airflow, DBT, Terraform, Databricks, PySpark

Cloud Platforms:

• AWS, GCP

Frameworks:

• Streamlit, Spring Boot, FastAPI, MLFlow

Work History & Key Achievements:

Machine Learning Engineer – Tech Company (Munich)

• Completed a Master Thesis on LLM Agent-Driven Tool Selection and Query Reformulation

• Worked with Graph Databases in Neo4j and Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG) for Knowledge Extraction, Retrieval, and Benchmarking

• Integrated Optical Character Recognition (OCR) and Visual Question Answering (VQA) into the document chunking pipeline to improve quality of Knowledge Extraction

• Built Python-integrated pipelines and Streamlit apps for KPI computation and fast experiments

• Merged unit tests into the deployment pipeline to aid Continuous Integration and Continuous Deployment (CI/CD)

• Developed modular services with RESTful API design using Spring Boot and Java

Data Scientist – Fantasy Gaming Platform

• 5th largest fantasy gaming platform in Asia

• Introduced the Form Index feature using Multi-class Supervised Learning (KNN, Support Vector Machines) and Unsupervised Learning (K-Means, Hierarchical Clustering) to classify players into quality levels based on advanced metrics – achieved F1 score of 0.85

• Leveraged probabilities and Linear Programming (PuLP) projections to deduce odds of athlete performances in real events, increasing engaged time per user per day by 15%

• Used Terraform to manage deployment and CI/CD practices in AWS production servers as part of MLOps

Senior Data Analyst – Web Consulting Company

• Led data-driven User Acquisition, Retention, and Monetization initiatives for a mobile strategy gaming app, delivering 120% increase in Paying User Conversion and 80% increase in 3-day Retention year-over-year

• Used Random Forest Classification and XGBoost to profile game users into categories to indicate likelihood of paying and retention

• Developed dashboards and mailers for cross-team use, saving 60 minutes per day per person

• Managed orchestration and ETL process of migrating from OLTPs to Google BigQuery as an alternate data source

Education:

• Master of Science in Data Engineering and Analytics

  • Winner – Analytics Cup (250 teams) powered by TUM and Siemens

  • Publications: Video Synthesis using Action Graphs for Surgery (MICCAI 2024)

  • Helped detect jamming and spoofing using GNSS for transportation and aerospace clients

• Bachelor of Technology in Information Technology

  • Final Project: IoT-based Automated Street Lighting System

  • Publication: “Honey-Bee Foraging Algorithm for Load Balancing in Cloud Computing Optimization”

Ideal For:

• Full-time Machine Learning Engineer / Data Engineer roles

• LLM, RAG, and Graph Database projects

• MLOps and cloud infrastructure (AWS/GCP)

• Fantasy gaming and user analytics

• Remote or Kolkata/Bangalore/Hyderabad/Munich-based opportunities

Important Notice : Candidate personal contact details including name, phone number, and email address are hidden for privacy and security reasons. Resume is strictly intended for legal hiring and recruitment purposes only. Any misuse, fraud, spam, illegal activity, or unauthorized distribution is strictly prohibited. Digital product only. No physical item will be shipped.