Reet Nandy, M.S.

Software + AI Engineer

Interested to see how I look? Click here :)

I believe in solving problems at scale.
FullStack? AI? Core/Infra?
I'm in! I Learn. I Implement.
Know more About Me and My Skills.

Currently, looking for SWE/AI May'25 full-time opportunities.

Prev, building Pricing Engine @ Mobility Intel, NYC.

reet.nandy@nyu.edu | +1 518-930-6116

GitHubLinkedInX/TwitterResume

LATEST* (Dec'24 onwards)

About Me

Reet Nandy

Hi, I'm Reet Nandy, a Software + AI Engineer with 3 years of experience across 6 internships specializing in Full Stack Development, Cloud/DevOps infrastructure, and AI/ML solutions. My expertise revolves around building scalable, cloud-native applications within distributed systems, optimizing performance, and developing and deploying AI models.

In the realm of AI, I specialize in working with traditional machine learning techniques to Large Language Models (LLMs), with a focus on building intelligent systems and optimizing end-to-end workflows.

  • Currently working on LLMs and RAG, focusing on multi-agent orchestration and advanced model pipelines.
  • Expertise in systems programming with C++, and backend development using Python (Django, Flask, FastAPI), Java (Spring Boot), and Node.js.
  • Strong front-end skills with Next.js, React.js, and Tailwind CSS.
  • Skilled in Relational, NoSQL, and caching technologies, along with vector databases.
  • Proficient in AWS with experience in containerization, orchestration, CI/CD pipelines, and implementing robust monitoring and alerting systems.

Education

  • M.S. in Computer Science, New York University (Expected May 2025)
  • B.Tech in Computer Science and Engineering, Manipal University Jaipur

Key Courses: Analysis of Algorithms, Operating Systems, Machine Learning, Cloud Computing, Big Data

Teaching Assistant: Algorithms (Fall 2024, Spring 2025), Operating Systems (Fall 2024)


If not a Coder, I would probably be a CHEF. I love cooking and I am great at it :)

View My Resume (PDF)

Skills

Languages

PythonC/C++JavaSQLJS/TSHTML/CSS

Backend

Django/Flask/FastAPINode.jsSpring BootServerlessREST APIGraphQL

Frontend

React.jsNext.jsTailwind CSSPyQTJava SwingFlutter

Database

PostgreSQLMongoDBRedisPineconeElasticsearchDynamoDBCassandra

DevOps/Cloud

AWSDockerKubernetesCI/CDJenkinsAirflowPrometheusKafka

AI/ML

Scikit-learnTensorFlowPyTorchMLFlowLLMsRAGVectorDB

Experience

Mobility Intelligence


FullStack Development Intern

June 2024 – December 2024

New York City, USA

Tech Stack:

PythonFastAPICeleryRedisPostgreSQLApache AirflowPrometheusGrafanaAWSKubernetes
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Key Responsibilities:

  • Built a real-time price prediction system using regression and Kalman filtering, achieving less than 5% error on 90-day forecasts.
  • Designed a FastAPI backend with Celery and Redis, handling 150k requests daily with 99.9% uptime and sub-500ms P95 latency.
  • Scheduled Airflow DAGs managing ETL pipelines processing 15M+ daily records from PostgreSQL into analytics-ready stores.
  • Configured Prometheus + Grafana with SLIs and alerting rules, reduced MTTD by 60% and improved response workflows.
  • Deployed microservices in AWS using Kubernetes with Helm charts, rolling updates, and horizontal pod autoscaling, reducing downtime during deployments by 80% and enabling seamless CI/CD.

Defence Research & Development Organisation


Software Engineering Intern (R&D)

January 2023 – June 2023

India

Tech Stack:

PythonRedisPostgreSQLPostGISLiDARETL
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Key Responsibilities:

  • Engineered multithreaded architecture for real-time LiDAR processing, handling 50K data points/sec (97% accuracy).
  • Implemented Redis-based geospatial caching over PostgreSQL/PostGIS, reducing GPS query latency from 1000ms to 150ms.
  • Developed ETL pipeline using memory-efficient streaming, processing 12GB/min while reducing memory usage by 60%.

Solar Industries India Ltd


Software Engineering Intern

April 2022 – December 2022

India

Tech Stack:

DjangoKafkaRedisCassandraJaeger
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Key Responsibilities:

  • Led a team of 5 to automate workflows, delivering 5 Django microservices that standardized 80% of manual processes.
  • Reduced API latency by 25% and integrated distributed tracing with Jaeger, enabling real-time debugging.
  • Designed a partitioned Kafka pipeline with scalable consumer groups, processing 2.5M+ rows/sec using Redis caching and Cassandra-backed storage.

Showing 3 of 6 internships

Projects

DocFlow: GraphRAG - LLM Document Compliance

Next.jsFastAPIGraphRAGPostgreSQLAnthropic APIFAISSNeo4J
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  • Launched an Agentic SaaS with real-time document edit, approval and audit reports via Graph based RAG and LLM.
  • Implemented a GraphRAG and PDF parser from scratch for unstructured PDFs using PDFMiner & Tesseract (OCR).
  • Executed semantic chunking + NLP NER + BART-CNN summarization, achieving 70% relation extraction accuracy.
  • Synthesized hybrid retrieval (Vector + Graph + metadata) boosting compliance accuracy to 90%.

VectorFlow: Hierarchical Vector Database (from Scratch)

PythonFastAPIPydanticCohereAsyncIODockerKubernetesHelmMinikube
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  • Built embedding database (library - document - chunk) with async collection mutexes; 12K ops/sec at <0.1% conflicts.
  • Added 3 indexing algorithms (LinearScan/KD-Tree/LSH) for vector search on 10M vectors in 18ms.
  • Led Kubernetes Helm deployment along with custom made CLI toolkit, reducing onboarding complexity by 100%.

GrantGenie: AI Agent for Web3 Global Fund Matching

FastAPIPostgreSQLRedisLangChainOpenAI APIPineconeweb3.pyIPFS
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  • Developed a Python backend using FastAPI, PostgreSQL, and Redis, enabling efficient API-driven grant discovery and data retrieval pipelines.
  • Built AI-driven grant matching with LangChain, OpenAI APIs, and Pinecone, leveraging LLMs and vector search for multilingual NLP, RAG-based personalized recommendations, and automated grant application drafting.
  • Integrated Web3 features with web3.py, Ethereum testnets, and IPFS, enabling wallet authentication, smart contract-based grant tracking, and decentralized metadata storage.

AI-Fitness Analytics Dashboard (AWS)

DjangoReactJsLambdaDynamoDBBeanstalkSagemakerSNSSQS
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  • Architected a scalable health tracking platform leveraging AWS services including SageMaker (KNN model), Lambda, SQS, SNS, RDS, DynamoDB, and S3, enabling real-time insights and personalized exercise recommendations.
  • Developed an ETL pipeline to synchronize health metrics from Google Fit API into DynamoDB and RDS, integrating microservices for data processing, storage, and ML-driven predictions.
  • Ensured secure and reliable operations with AWS Cognito for authentication, CloudWatch for monitoring, and IAM for access control, alongside encrypted data at rest and in transit.

KubeControl: Cloud-Native Monitoring and Alerting Solution

FlaskMongoDBDockerKubernetes (Minikube, AWS EKS)PrometheusSlack APIAWS (EKS, S3, CloudWatch)
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  • Designed and deployed a scalable Flask-MongoDB application using Docker and Kubernetes, with advanced features like rolling updates, replication, and health probes for robust orchestration.
  • Implemented real-time monitoring and alerting by integrating Prometheus and Slack, enabling proactive issue resolution in both local (Minikube) and cloud (AWS EKS) environments.
  • Leveraged AWS services, including EKS for orchestration and S3 for storage, ensuring production-grade deployments with seamless scalability and high availability.

Showing 5 of 14 projects

Resume

Download my Resume Here: Software + AI Engineer

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