Lakshay Arora

PhD | AI/ML Researcher Engineer | Turning Research Into Scalable Solutions

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About Me

I’m a Ph.D. with 5+ years of experience applying AI and machine learning to real-world challenges across finance, healthcare, and engineering. My work spans traditional machine learning, deep learning, reinforcement learning, generative AI, and optimization under uncertainty—translating research into impactful, data-driven solutions. I enjoy building intelligent systems that are both innovative and practical.

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Experience

ML Engineer

BenchSci
Nov 2022 – Present | Toronto, Canada

  • Built RAG and agentic RAG applications over large-scale scientific data using embedding models (e.g., text-embedding-ada-002) with semantic splitting and hybrid chunking to improve retrieval quality.
  • Orchestrated multi-agent workflows with LangGraph and CrewAI for dynamic retrieval, reasoning, tool invocation, and human-in-the-loop oversight via MCP-integrated APIs and databases.
  • Dockerized and optimized Llama 3.1–based services with quantization, KV-cache reuse, and GPU acceleration, reducing end-to-end inference latency by ~60%.
  • Tools: LangGraph, CrewAI, LangChain, Hugging Face, PyTorch, Transformers, MCP, Azure ML, vector DBs, FastAPI, Docker, CUDA.

Applied Machine Learning Researcher

Spacecraft Robotics Laboratory, Carleton University
Sep 2020 – Present | Ottawa, Canada

  • Designed spacecraft guidance policies under uncertainty using Koopman Expectation and nonlinear optimization.
  • Integrated deep learning models (PyTorch) with Julia-based simulators for trajectory generation.
  • Improved guidance accuracy by 84% and reduced simulation runtime by 50%.
  • Tools: Julia, Python, PyTorch, MATLAB, Simulink.

Data Analysis Assistant

Independent Consultant – Part-time
March 2025 – July 2025 | Toronto, Canada

  • Built Python-based transaction rule filters to support AML compliance for a prominent Canadian bank’s review process.
  • Applied financial heuristics and backend data analysis to identify suspicious activity patterns.
  • Accelerated false-positive triage by 28%, enhancing review speed and detection efficiency.
  • Tools: Python, Excel, AML backend system.

AI/ML Research Associate

AI Quest Inc. & George Brown College (Mitacs BSI)
May 2022 – Sep 2022 | Toronto, Canada

  • Developed an NLP-based pharmacovigilance system using Python and Twitter API.
  • Processed 30GB+ of pharma and social data to detect ADRs with a 15% improvement in prediction accuracy using deep learning.
  • Delivered dashboards for stakeholders to support real-time healthcare monitoring and drug safety reporting.
  • Tools: Python, Pandas, Tweepy, NLTK, Deep Learning.

Machine Learning Researcher

Wichita State University
Sep 2017 – Feb 2020 | Kansas, USA

  • Applied reinforcement learning to the spacecraft orbit-raising problem by designing a reward-adaptive control framework.
  • Formulated dynamic cost reweighting strategies to balance fuel consumption and time-of-flight in long-duration transfers.
  • Improved simulation efficiency and fuel optimization by up to 18% through deep Q-learning in MATLAB.
  • Tools: Python, MATLAB, Simulink.

Data Analyst / Data Scientist

Albatronix
Sep 2016 – Jul 2017 | Bengaluru, India

  • Designed regression and predictive models to optimize sales incentive planning, production forecasting, and financial sensitivity analysis by customer segment.
  • Built ETL pipelines and automated ingestion/cleaning of structured and unstructured datasets using Python and SQL Server, enabling reproducible modeling workflows.
  • Delivered Tableau and Power BI dashboards to surface KPIs, trends, and forecasting outputs for sales and operations stakeholders.
  • Tools: Python, scikit-learn, Pandas, NumPy, SciPy, SQL Server, Tableau, Power BI, Django.

Projects

SRL Assistant

Spacecraft Robotics Laboratory AI Assistant

Streamlit app for AI assistant that streamlined research knowledge for my research lab SRL using a RAG pipeline (Gemini + structured parsing).

Live Site
Cost of Living Tool

Student Cost-of-Living Calculator

Streamlit app for Canadian students to estimate expenses using Gemini Pro AI.

Live Site
GitHub Repo
Pharmacovigilance Project

Pharmacovigilance via Twitter NLP

Built during Mitacs Internship (Mitacs BSI), this project leveraged Natural Language Processing to extract and analyze adverse drug reactions (ADR) from tweets for pharmacovigilance applications. Processed over 30GB of data to improve ADR prediction accuracy by 15%.

GitHub Repo
Rendezvous Guidance

Deep RL for Spacecraft Rendezvous

Reinforcement learning-based trajectory optimization under uncertainty.

GitHub Repo
Flight Fare Prediction

Flight Fare Prediction using ML

A complete end-to-end project to predict the domestic flight prices in India depending on various features using Random Forest Regressor and XGBoost Regressor which is then deployed as a Flask Web Application on Render.

Live Site
GitHub Repo
Robotic Arm

Adaptive Robotic Arm Control

Adaptive control for 2-DOF robotic arm with uncertain payloads.

GitHub Repo
HR Analytics Project

HR Analytics for Retention

Analyzed HR data using classification models to identify key employee attrition factors and predicted churn risk. Delivered actionable insights to improve retention strategies for organizations.

GitHub Repo

Skills

Programming Languages

  • Python
  • Julia
  • MATLAB
  • SQL (MySQL, PostgreSQL)
  • C++

Machine Learning & AI

  • Deep Learning (PyTorch, TensorFlow, Keras)
  • Generative AI & LLMs
  • RAG & Agentic RAG Architectures
  • Reinforcement Learning (RLHF, QLoRA/LoRA)
  • Time Series Modeling (ARIMA, SARIMA, SARIMAX)

Agentic AI & LLM Frameworks

  • LangChain, LangGraph, CrewAI, AutoGen
  • Hugging Face Transformers
  • Model Context Protocol (MCP)
  • Pydantic, LangFlow

Cloud Platforms

  • Google Cloud (Vertex AI, Model Garden, Cloud Functions, Firestore)
  • AWS (SageMaker, Bedrock, Lambda, DynamoDB, API Gateway, S3)
  • Azure ML (for RAG/agentic deployments)

LLM APIs & Deployment

  • OpenAI, Gemini, Anthropic, Perplexity, Grok, Groq
  • FastAPI, Docker, CI/CD (GitHub Actions)
  • Serverless & containerized deployment
  • Quantization, pruning, distillation, CUDA acceleration

Data Tools & Databases

  • Pandas, NumPy, Scikit-learn
  • MongoDB, Cassandra
  • Power BI

Prompt Engineering

  • Chain of Thought, Few-Shot, Self-Consistency
  • ReAct (Reason + Act)

NLP & Text Processing

  • NLTK, TextBlob, VADER
  • Gensim, spaCy

Visualization & Reporting

  • Tableau, Power BI
  • Matplotlib, Seaborn
  • LaTeX

Education

Ph.D. in Aerospace Engineering

Carleton University — Ottawa, Canada
2020 – 2025

Focus: Spacecraft Guidance, Path Planning under Uncertainty, AI in Autonomous Space Missions.

MS in Aerospace Engineering

Wichita State University — Kansas, USA
2017 – 2020

Thesis: Reinforcement learning framework for spacecraft low-thrust orbit raising
View Thesis

B.Tech in Aeronautical Engineering

Manipal Institute of Technology — India
2013 – 2017

Certifications

IBM Certificate

IBM Data Science

Specialization on Coursera

View Certificate
IMS Certificate

Business Analytics

By IMS Proschool

View Certificate

Publications

AAS Conference

Koopman Expectation-Based Guidance for Spacecraft Rendezvous and Proximity Operations under Uncertainties

35th AAS Space Flight Mechanics Conference (Accepted)

SciTech

Reinforcement Learning for Sequential Low-Thrust Orbit Raising Problem

AIAA Scitech 2020 Forum

Paper Link
AAS

Objective function weight selection for sequential low-thrust orbit-raising optimization problem

AIAA/AAS Space Flight Mechanics Meeting

Paper Link
Download Resume

Blog Posts

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Building LLMs from Scratch: Chapter 1 Reflections

A beginner-friendly breakdown of key takeaways from Chapter 1 of Sebastian Raschka’s "Building LLMs from Scratch".

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Building LLMs from Scratch: Chapter 2 Reflections

Chapter 2 — Tokenization: Breaking Language Into Lego Bricks.

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Building LLMs from Scratch: Chapter 3 Reflections

Chapter 3 - Attention: Teaching Models What to Focus On".

Read on Medium

Contact

Email: lakshayarora2701@gmail.com

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