Hi, I'm Saurabh Singh AI Engineer

Software Engineer specializing in Data & AI, building enterprise-grade AI applications and data infrastructure that drive business outcomes at scale.

2+ Years Experience
5+ AI Projects
VP Level Recognition
View My Work Get In Touch
ai_engineer.py 
class AIEngineer:
    def __init__(self):
        self.expertise = [
            "LLMs & RAG Systems",
            "Data Pipelines",
            "Enterprise AI",
            "Full-stack Development"
        ]
        self.impact = "Production-grade solutions"
    
    def solve_problems(self):
        return "AI-driven innovation"

Awards

Recognition for engineering impact and innovation

Dataverse Business Impact Award

Red Hat

Q2 2025

Award

Best Engineering Innovation in Dataverse — AI. Recognized for developing an intelligent assistant that empowers leaders to extract insights from the enterprise data platform via conversational, context-aware interfaces. The prototype was built in under two days, demonstrating rapid innovation and proving viability. Impact areas include reduced time-to-insight, higher trust in AI-assisted tools, and increased productivity for leadership.

Highlights

  • LLM-powered assistant utilizing software agent paradigms and multi-component patterns
  • Shift from static dashboards to dynamic conversational interfaces
  • Strong positive feedback from key stakeholders

About Me

Building the future with AI and data

I'm a passionate Software Engineer specializing in Data & AI with a proven track record of delivering enterprise-grade AI applications that receive executive-level recognition. My journey spans from academic research to building production systems that solve real-world business problems.

Currently, I design and build full-stack AI applications and data infrastructure, focusing on LLMs, RAG systems, and automated data pipelines. My work has consistently delivered significant business impact, including reducing complex manual processes from months to weeks using AI-assisted methodologies.

Key Strengths

Rapid Innovation

Praised by VPs for quick iterations and bringing velocity to AI initiatives

Business Impact

Connecting technical solutions to real business problems and strategic goals

Enterprise Quality

Building production-ready, scalable solutions that integrate into core workflows

Cross-functional Leadership

Collaborating across teams and driving adoption of AI solutions organization-wide

Professional Journey

2024 - Present

Software Engineer, Data & AI

Red Hat — Leading enterprise AI initiatives, building production-grade applications

2024

Research Assistant

Advanced RAG research for healthcare applications at Boston University

2022 - 2024

Graduate Studies

MS Computer Science, Boston University - Focus on AI/ML

Saurabh Singh

Saurabh Singh

Software Engineer, Data & AI

100% Project Success Rate
VP+ Executive Recognition

Core Technologies

🐍 🔥 ⚛️ 🐳 ☸️ 🐘

Professional Experience

Building AI solutions that drive business outcomes

Software Engineer, Data & AI

Red Hat (Boston, MA)

July 2025 – Present

Current Role

Design and build full-stack AI applications and data infrastructure to accelerate data-driven initiatives.

Key Achievements

VP+
Executive recognition for AI innovation velocity
6mo→3wk
Reduced complex analysis timeline using AI
100%
Production deployment success rate

Core Responsibilities

  • Building full-stack AI applications on top of data systems like Snowflake, Atlan, and GitLab via MCP servers to drive Red Hat’s data initiatives
  • Led development of an AI assistant to streamline data access via MCP servers on 100+ databases in Snowflake using AI‑generated SQL
  • Created a natural language query AI interface for Snowflake that empowers business users to self‑serve data with over 90% accuracy
Python FastAPI LLMs RAG React Data Pipelines SQL Docker

Data Engineer

Red Hat (Boston, MA)

Jan 2025 – July 2025

Data

Delivered reliable data products and pipelines as part of a modern data platform.

Core Contributions

  • Designed and optimized data pipelines using dbt, Snowflake, and advanced SQL, improving transformations and streamlining ETL workflows for scalable operations across business units
  • Delivered source‑aligned data products and aggregated data from multiple sources, ensuring consistency and accuracy to support BI and decision‑making
SQL Data Warehousing dbt Python

Data and AI Intern

Red Hat (Boston, MA)

May 2024 – Dec 2024

Internship

Developed RHContract.AI, a large‑scale contract discovery solution using LLMs.

Key Contributions

  • Reduced contract document processing time by 90% by implementing contract filtering, extraction, and signature detection on 300k PDFs (156GB), improving efficiency for Sales
  • Achieved over 90% accuracy in attribute extraction and up to 95% in signature detection by leveraging multimodal LLMs
LLMs RAG Vector DB Python

Research Assistant

Boston University

Feb 2024 - May 2024 · 4 mos

Research

Research on AI-driven solutions leveraging LLMs, vector embeddings, and RAG for healthcare guideline generation.

Research Focus Areas

  • Advanced Retrieval-Augmented Generation (RAG) techniques and optimization strategies
  • Query decomposition and translation methodologies for multi-database systems
  • Specialized embedding models and hierarchical indexing architectures
  • Self-RAG and corrective RAG implementations for improved generation quality
  • Vector database optimization and semantic routing for healthcare applications
LLMs Vector Embeddings RAG Graph Databases Vector Databases NLP

Graduate Student

Boston University

2022 - 2024

Education

Master of Science in Computer Science with focus on Machine Learning and AI research.

Academic Achievements

Published research in ICONIP 2022 (International Conference on Neural Information Processing)
Developed novel adaptive early classification model for time series
Research on driving behavior analysis using deep learning on GPS data
Deep Learning CNN/RNN Reinforcement Learning Time Series Computer Vision

Technical Expertise

Cutting-edge technologies for AI and data solutions

AI & Machine Learning

🤖

Large Language Models

GPT, Gemini, Claude, Fine-tuning, Prompt Engineering

🔍

RAG Systems

Vector embeddings, Semantic search, Multi-modal RAG

🧠

Deep Learning

TensorFlow, PyTorch, CNN, RNN, Transfer Learning

📊

ML Operations

Model deployment, MLflow, Monitoring, A/B testing

Data Engineering

🔄

Data Pipelines

ETL/ELT, Real-time processing, Event-driven architecture

🗄️

Databases

PostgreSQL, Vector DBs, Graph DBs, NoSQL

☁️

Cloud Platforms

AWS, Docker, Kubernetes, Serverless

📈

Analytics Tools

Snowflake, dbt, Apache Spark, Data warehousing

Software Development

🐍

Backend Development

Python, FastAPI, Node.js, Microservices

⚛️

Frontend Development

React, TypeScript, Modern CSS, Responsive design

🔧

DevOps & Tools

Git, CI/CD, Testing, Monitoring, Agile

🏗️

System Design

Scalable architecture, API design, Security

Proficiency Levels

Python & AI/ML 95%
Data Engineering 90%
Full-stack Development 85%
Cloud & DevOps 80%

Featured Projects

Enterprise-grade AI solutions with proven business impact

Production

Enterprise AI Assistant Platform

Sophisticated AI assistant platform with specialized capabilities for data interaction and knowledge base queries. Features human-in-the-loop security model and scalable three-tier architecture.

VP+ Executive Recognition
4K+ Lines of Code
100% Deployment Success
Python FastAPI LLMs React Vector DB
Production

Snowgenie: Natural Language Data Access

Web-based AI platform enabling non-technical users to query large datasets using natural language. Open-source project focused on self-serve analytics and safe NL-to-SQL.

Risk Mitigation
Self-Service Data Access
NLP SQL Generation React Cloud DB
View on GitHub
Completed

AI-Assisted Legacy System Analysis

Pioneered AI-assisted methodology for reverse engineering complex legacy data pipelines. Condensed 6-month manual process into weeks, enabling strategic system migration planning.

6mo→3wk Time Reduction
80+ Components Analyzed
AI Analysis System Migration Data Mapping Python
Research

Advanced RAG for Healthcare

Research on AI-driven solutions leveraging LLMs, vector embeddings, and sophisticated RAG techniques for healthcare guideline generation. Explored multiple retrieval optimization strategies.

Multi-DB Query Support
Healthcare Application
RAG Vector Embeddings Graph DB LLMs
View on GitHub
Published

Adaptive Early Time Series Classification

Novel deep learning model combining CNN, RNN, and reinforcement learning for early time series classification. Published in ICONIP 2022, outperformed state-of-the-art in accuracy and earliness.

ICONIP 2022 Published
SOTA Performance
Deep Learning CNN/RNN RL Time Series
View Paper
Benchmark

Transfer Learning on CIFAR-10

Benchmarked InceptionV3, ResNet50, and VGG19 using advanced fine-tuning strategies (gradual unfreezing, discriminative LR, scheduling). InceptionV3 achieved 95.44% accuracy and 0.9567 F1; ResNet50 achieved 94.11% accuracy and 0.9415 F1; VGG19 achieved 93.31% accuracy and 0.9327 F1, highlighting architecture–dataset fit.

95.44% Top Accuracy
0.9567 Top F1
3 Models Benchmarked
Transfer Learning CNN TensorFlow/PyTorch
Read Report
More

Additional Projects

Selected technical projects across AI, data, and systems:

  • Offline Chatbot with RAG on PDFs and Webpages
  • Advanced MNIST Classification and Dimensionality Reduction
  • Advanced Query Processing with Spark and OpenAI
  • Kafka-Airflow Pipeline for Real-Time Streaming
  • Optimal Gameplay using Reinforcement Learning
  • Exploratory Data Analysis and Sampling
  • LLM Inference Time and Resource Usage Analysis (3B–70B)
  • Heart Disease Prediction using Logistic Regression
Browse GitHub

Research & Publications

Contributing to the advancement of AI and machine learning

Adaptive early classification of time series using Deep Learning

ICONIP 2022 2022 Conference Paper
A Sharma, Saurabh Singh, Abhinav Kumar, Amit Singh, and Sanjay Singh

Proposed an adaptive early classification model composed of a base classifier designed as a hybrid model of Convolutional Neural Network and Recurrent Neural Network, with a reinforcement learning decision policy for adaptive halting capabilities. The model outperformed state-of-the-art alternatives in both accuracy and earliness.

Outperformed SOTA
Novel Architecture
Early Classification
View Paper

Driving behavior analysis using Deep Learning on GPS data

EGTET 2022 2022 Conference Paper
Saurabh Singh, Utkarsh Anand, Anurag Patel, and Debojit Boro

Novel feature extraction model using statistical approach to extract important features from GPS trajectory data and classify safe vs unsafe driving behavior. Applied deep learning model with reduced computational overhead while maintaining high accuracy for dangerous driving pattern detection.

Traffic Safety
High Performance
GPS Analytics
View Chapter

Research Interests

Large Language Models

Advancing LLM capabilities for enterprise applications

Retrieval-Augmented Generation

Optimizing RAG systems for domain-specific applications

Time Series Analysis

Early classification and forecasting methodologies

AI for Healthcare

AI-driven solutions for medical guideline generation

Let's Connect

Ready to build the future of AI together

Email

ssmail@bu.edu

LinkedIn

linkedin.com/in/saurabh-sks

GitHub

github.com/Saurabh-Singh-55

Location

Boston, MA

Interested in collaboration?

I'm always excited to discuss AI innovations, data engineering challenges, and opportunities to build impactful solutions. Whether you're looking for technical expertise or want to explore potential collaborations, let's connect!

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