Ajay Singh

Software Engineer · Data Platforms & AI

Building large-scale data pipelines and lakehouse systems. Currently focused on the intersection of data engineering and AI.

// Professional profile

const engineer = {

current_role:"UBS · Data Platforms",

location:"Pune, India",

stack:["PySpark", "Kafka", "Delta Lake", "FastAPI"],

};

[GitHub ↗]

02 // ABOUT

I am a Software Engineer with 3 years building enterprise data platforms at UBS and Credit Suisse. My work sits at the intersection of large-scale data engineering and AI — designing lakehouse architectures, real-time streaming systems, and the data infrastructure that powers ML and LLM applications.

$ cat tools_and_technologies.json

>_Languages

  • Python
  • SQL
  • C++

>_Data Eng

  • PySpark
  • Kafka
  • Delta Lake
  • dbt
  • Airflow

>_Cloud & Infra

  • Azure Databricks
  • ADLS
  • ADF
  • Docker
  • GitLab CI/CD

>_Backend

  • FastAPI
  • REST APIs
  • Microservices

03 // EXPERIENCE

Software Engineer, Data Platforms

@ UBS
Nov 2024 – Present
  • Lakehouse pipelines on Azure Databricks processing 50M+ records/day across 6 applications and 200+ downstream consumers
  • Built Kafka DataMesh notification framework with exactly-once processing; cut critical pipeline latency from 8 hours to 90 minutes
  • Improved platform DR coverage from 40% to 90%; co-designed enterprise metadata catalog using DCAT/DQV standards

Data Engineer (Technology Analyst)

@ Credit Suisse
Jul 2023 – Nov 2024
  • ETL pipelines for analytics and reporting in a large-scale data warehouse
  • Data modeling, schema design, and APIs serving datasets to downstream apps

04 // PROJECTS

ReactFastAPIPostgreSQLAzure OpenAI

Promptathon (UBS Hackathon)

INTERNAL

LLM-powered prompt evaluation platform with automated scoring and real-time leaderboard. Built at UBS internal hackathon; reached semi-finals at UBS Global Hackathon 2023 & 2024.

[GitHub 🔒 (Internal Project)]

More coming soon

Building in public — projects dropping soon.

05 // CONTACT

Get in touch

I am always open to discussing data platform architectures, lakehouse systems, or potential collaborations at the intersection of data engineering and AI.