Hi, I’m
Arul JC
Newyork,United States
Summary
GenAI Engineer and Data Scientist with a proven track record in designing, developing, and deploying end-to-end AI/ML solutions at scale. Deep expertise in Large Language Models including LLaMA 2, GPT-4, and Gemma. Skilled in model training, fine-tuning, and evaluation, with proficiency in deep learning frameworks like TensorFlow and PyTorch. Demonstrated success in building scalable machine learning pipelines, deploying production-grade AI systems, and leveraging cloud platform like AWS) for real-time AI applications.Seeking a challenging and rewarding role at a leading technology company where I can contribute to innovative AI products and advancements.
Overview
4
years of professional experience
Work History
Upbound INC
Machine Learning Engineer
07.2025 - Current
Job overview
- Designed and automated a document classification and extraction pipeline for highly unstructured, multiregion documents using Tesseract OCR and rule-based methods to identify valid clauses.
- Worked directly with customer and business teams to gather requirements, deliver AI solutions, and provide ongoing technical support.
- Designed and implemented end-to-end ETL pipelines for highly unstructured, multi-region enterprise documents, enabling downstream Agentic AI and analytics workflows.
- Built scalable data ingestion, transformation, and enrichment pipelines integrating OCR, structured extraction, and semantic embeddings.
- Developed LLM-powered RAG and validation workflows, allowing AI agents to retrieve, reason over, and act on governed enterprise data.
- Implemented API-first data services using FastAPI and AWS Lambda, enabling AI agents and applications to access structured and semantic data in real time.
- Designed containerized model and data serving architectures using Docker and Kubernetes to support both batch and low-latency inference workloads.
- Established data quality checks, validation rules, and observability pipelines using MLflow, Kibana, and Grafana to monitor data drift, anomalies, and system health.
- Collaborated with architects, software engineers, and data scientists to align data infrastructure with Agentic AI system requirements and business objectives.
- Led DevOps practices for containerized AI platforms using Kubernetes, Docker, Terraform, and CI/CD pipelines, with production-grade monitoring and observability.
Copart
Data Scientist Consultant
08.2023 - 06.2025
Job overview
- Architected and developed Purple Fabric, a scalable, AI-native SaaS platform designed for high-value horizontal use cases (e.g., intelligent search, automated workflows).
- Deployed Kubernetes-based platforms adaptable to both cloud and on-prem enterprise environments.
- Designed and deployed scalable Deep Learning solutions for Natural Language Processing (NLP) and Generative AI applications.
- Built LLM-based AI Guardrails for Prompt Injection Detection, Personally Identifiable Information (PII) masking, and Toxic Content Filtering using Meta LLaMA 2 70B, GPT-4o, SETFIT models and AWS Comprehend. Applied synthetic data generation via Gemma 2B for anonymization.
- Engineered citation backfilling to validate and attribute references used in generative responses, increasing transparency and factuality.
- Created a document classification and extraction model based on LayoutLM for automating categorization of emails and financial reports, improving efficiency by 75%.
- Designed LLM benchmarking tools using LLM as Judge, ROUGE-L, fuzzy JSON, exact match scoring, and accuracy metrics.
- Automated end-to-end error tracking and reporting workflows to monitor system anomalies across environments, enabling reduction of total errors from 6% to under 1% within a quarter, recognized by senior leadership for operational excellence.
- Designed and implemented an AI-native support assistant leveraging LLMs to accelerate product issue resolution and enhance client support efficiency.
- Designed scalable Kubernetes-based AI platforms for LLM inference, RAG pipelines, and agentic workflows.
- Partnered with security teams to enforce enterprise AI governance, privacy controls, and compliance guardrails.
LTI Mindtree
Data Scientist
01.2022 - 11.2022
Job overview
- Automated Infrastructure as Code (IaC) solutions using Terraform for AWS resource provisioning (EC2, S3, IAM, Lambda, RDS).
- Built cloud-native ETL pipelines using Python, SQL, AWS, and Azure for automated data ingestion, transformation, and quality validation.
- Designed data models and feature pipelines to support machine learning, analytics, and reporting workloads.
- Implemented Infrastructure as Code (Terraform) for provisioning secure, scalable cloud data platforms (EC2, S3, IAM, Lambda, RDS).
- Performed exploratory data analysis (EDA) to identify trends, correlations, and data quality issues for business stakeholders.
- Developed and deployed ML pipelines for classification, regression, and forecasting, including monitoring and versioning using MLflow and Git.
- Created data visualizations and dashboards using Tableau / Power BI to communicate insights to technical and non-technical stakeholders.
- Collaborated cross-functionally with engineers, architects, and analysts to deliver enterprise-grade data and AI solutions.
Education
Lindsey Wilson University
Masters in Technology Management from Data science
01.2024
University Overview
GPA: 3.30/5.00
Vellore Institute of Technology UniversityAmaravati
B.B.A from Business Administration
01.2022
University Overview
- Relevant Coursework: Data Systems, Data Algorythms, Linear algebra, Object Oriented Programming, Databases Management, Discrete Mathematics, Operating Systems, Computer Networks, Machine Learning, Data Mining, Cloud computing
- GPA: 8.00 / 10
Skills
- Programming Languages & Frameworks: Python (NumPy, Pandas, Scikit-learn, Keras, matplotlib, Flask, FastAPI), PyTorch, TensorFlow
- Databases: SQL, MongoDB
- Artificial Intelligence & Machine Learning: Machine Learning: Supervised Learning (Decision Trees, Random Forests), Unsupervised Learning, Data Modeling & Evaluation, Preprocessing & Postprocessing, Model Optimization & Performance Tuning Deep Learning: Convolutional Neural Networks (CNNs), Long ShortTerm Memory (LSTM), Gated Recurrent Units (GRU) Gen AI: Prompt Engineering, LLM Fine-Tuning, Retrieval-Augmented Generation (RAG), ReAct
- LLM Experience: Meta LLaMA 2, Google Gemma, OpenAI GPT-4, Anthropic Claude
- Cloud & DevOps: Amazon Web Services (EC2, S3, IAM, Lambda, RDS, Sagemaker), Google Vertexai, Docker, Kubernetes, Jenkins, Git, Postman, Kubernetes (OpenShift-compatible), Docker Helm, Terraform, Jenkins, CI/CD Pipelines, Infrastructure as Code (IaC), On-Prem & Hybrid Kubernetes Environments
- Tools: Visual Studio code, Jupyter Notebook, CVAT, PuTTY, Tableau, Langfuse, Mlflow, Kibana, Grafana, Jira, Azure Devops ADO & confluence for project management
- Machine learning
- Natural language processing
- Feature engineering
- Model development
- Clustering algorithms
- Random forests
- Decision trees
- Transfer learning
- Data analytics
- Data mining
- Statistical modeling
- Dimensionality reduction
- Reinforcement learning
- Support vector machines
LEADERSHIP & AWARDS
Tech club head, Lindsey Wilson, TedxVITAP Org, Event Manager, Null Chapter Club – VITAP, Member, Bulls and Bears Finance Club, Volunteer, Intellect Fest 2023
Languages
English
Full Professional
Spanish
Professional Working
Hindi
Full Professional
Timeline
Machine Learning Engineer
Upbound INC
07.2025 - Current
Data Scientist Consultant
Copart
08.2023 - 06.2025
Data Scientist
LTI Mindtree
01.2022 - 11.2022
Vellore Institute of Technology University
B.B.A from Business Administration
Lindsey Wilson University
Masters in Technology Management from Data science