Anirudh Pratap Singh

Software Engineer

Noida, Uttar Pradesh, India1 yr 2 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in building AI-driven tools for automation.
Proficient in Retrieval-Augmented Generation and Generative AI.
Strong background in scalable backend infrastructure.

Stackforce AI infers this person is a SaaS-focused engineer specializing in AI systems and scalable backend solutions.

Contact

Skills

Core Skills

Retrieval-augmented Generation (rag)Generative AiNeural Networks

Other Skills

DockerEvaluation MetricsFastAPILangChainLarge Language Model Operations (LLMOps)SQLTypeScript

About

I'm Anirudh, an engineer focused on building secure, intelligent systems at the intersection of Agentic AI, Retrieval-Augmented Generation (RAG), and scalable backend infrastructure. I design full-stack, cloud-native applications using TypeScript, Next.js, and Dockerized microservices on AWS/GCP. My core strength lies in creating enterprise-grade AI copilots, semantic RAG systems, and custom AI agents powered by LangChain, LlamaIndex, Qdrant, and LLMs. I’ve built AI-driven tools that cut down manual ops, enable real-time information retrieval, and automate decisions including semantic search platforms, multi-format document classifiers, and custom agent workflows using LangGraph. I'm deeply interested in solving performance and scaling challenges, integrating vector DBs like AstraDB, and defending GenAI systems against prompt injection and hallucination through observability and prompt tooling. Let's connect if you're building in GenAI, enterprise AI copilots, or looking to scale AI workflows with secure infra. Projects & code: github.com/anirudhpratap345

Experience

Stealth startup

Developer

Jul 2025 – Present · 8 mos

Data security council of india

SDE Intern

Jan 2025 – Jul 2025 · 6 mos · Noida, Uttar Pradesh, India · On-site

Built an AI-driven pipeline for automated data extraction, enrichment, and capability mapping of 6000 plus Indian tech entities, using structured JSON outputs and a modular dashboard to streamline validation and data flow.
Designed a semantic matching and RAG-based AI search system using Sentence Transformers for embedding, Qdrant as the vector store, and LLMs for context-aware query responses; integrating ingestion and retrieval pipelines to reduce manual mapping effort by over 70%.

TypeScriptLarge Language Model Operations (LLMOps)DockerRetrieval-Augmented Generation (RAG)SQLEvaluation Metrics+2

Innovians technologies

Summer Intern

May 2024 – Jul 2024 · 2 mos · Gurugram, Haryana, India · Hybrid

Built and optimized an AI-powered document classification tool using Logistic Regression, KNN, and Neural Networks, achieving high precision and efficiency.
Enhanced model accuracy by implementing advanced text preprocessing techniques, including tokenization, stopword removal, and TF-IDF vectorization.
Deployed the tool to achieve up to 50% faster document processing compared to manual methods, optimizing overall workflow.

FastAPINeural Networks