Aditya
Backend & Applied AI Engineer · Node | Go | Python (FastAPI) | Qdrant Vector DB | Redis Streams | PostgreSQL
Building production-focused Backend & Applied AI systems. Creator of ArchadiLM (Multi-Tenant RAG Engine) and ByteVault (Cloud Native Storage Platform).
Technical Skills
Technologies I Work With
Technologies I use professionally and the areas I'm currently exploring to build reliable backend systems.
// Featured Work
Production Systems & AI Architecture
High-throughput backend microservices, async event processing, vector engines, and Applied AI systems engineered for scale.
ArchadiLM
FlagshipEnterprise Multi-Tenant RAG & Knowledge Workspace
A high-performance Retrieval-Augmented Generation (RAG) platform designed to index multi-modal knowledge materials (PDFs, VTT/SRT transcripts, ZIP archives, web URLs) with realtime SSE streaming and exact grounded source lineage.
Technologies & Infrastructure
System Topology & Data Flow
User / Web Client (Next.js)
│
▼
Go API Gateway (8081) ──► Magic Byte & Security Validation
│
┌────────┴────────┐
▼ ▼
Redis Streams Python AI Microservice (8000)
(pipeline:upload) (HyDE + RRF + Reranker)
│ │
▼ ▼
3-Stage Workers Qdrant Vector Store (HNSW Index)
(Manifest/Processor) │
│ ▼
└──────────► PostgreSQL (Metadata & Lineage)
- ▸Mitigating hallucinations & ensuring strict source-grounded responses across multi-file formats
- ▸Handling long-running document extraction & embedding without blocking API threads
- ▸Deduplicating retry chunks during worker retries without corrupting vector indices
Other Production Platforms
Win-Win Marketplace
AI-powered marketplace platform for vehicles, electronics and consumer goods.
Multi-tenant Hotel SaaS Builder
A multi-tenant SaaS platform enabling hotels to construct white-labeled websites, handle bookings, and process payments.
Healthosyst Platform
Healthcare management SaaS platform featuring realtime patient tracking, appointment scheduling, and automated notifications.
// System Design & Architecture
How The Systems Are Built
Deep dive into the underlying distributed pipelines, vector retrieval engines, state machines, and security mechanisms.
Multi-Stage RAG & Vector Search (ArchadiLM)
Hypothetical Document Embeddings (HyDE) + Reciprocal Rank Fusion + Reranking
When a user submits a query, it undergoes parallel query expansion (Step-Back + Sub-queries) and HyDE document synthesis. Batch vectors are searched across Qdrant using HNSW indices, merged via Reciprocal Rank Fusion (RRF), re-scored via Cross-Encoder Reranking, and streamed back via SSE with grounded file/timestamp citations.
Interactive Topology Diagram
User Query ("How do API routes work?")
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Go API Gateway (8081)
│
┌────────────────────────┴────────────────────────┐
▼ ▼
Query Enhancement (Step-Back) HyDE Generation (Hypothetical Doc)
│ │
└────────────────────────┬────────────────────────┘
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Batch Embeddings (OpenAI API)
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Parallel Search in Qdrant Vector DB
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Reciprocal Rank Fusion (RRF Merge)
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Cross-Encoder Reranking & Deduplication
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Grounded SSE Stream Response
PROFESSIONAL JOURNEY
From IT Support to Backend Engineering
My career started in enterprise IT support and gradually evolved into software engineering. Every step has been driven by curiosity, continuous learning, and building real-world systems.
Graduate Trainee
July 2022Wipro
Started my professional career in Enterprise IT Support, working on incident management, troubleshooting, production support and system reliability.
Transition to Software Development
December 2023Self Learning
Left IT operations and started learning software development from scratch, focusing on fundamentals before frameworks.
Full Stack Developer Intern
February 2025Production Internship
Worked on multiple production applications, shipping features, fixing bugs and maintaining real-world applications deployed using cPanel.
Software Engineer
August 2025Backend Team
Converted into a full-time Software Engineer and started contributing to backend services, APIs and production systems using Node.js and FastAPI.
Building ByteVault
PresentPersonal Engineering
Outside of work, I'm building ByteVault while learning Go, Distributed Systems, Cloud Infrastructure and Generative AI.
Current Focus
Building for the Next Chapter
Professionally, I contribute to backend systems using Node.js, Express and FastAPI. Personally, I'm investing my time in Go, Distributed Systems, Cloud Infrastructure and building ByteVault — a cloud-native file storage platform designed with scalability and production engineering principles in mind.
By The Numbers
Engineering Highlights
Engineering Notes
Learning in Public
Notes from building production systems, learning Go, distributed systems, AI engineering and everything in between.
Building ByteVault in Public
A deep dive into every engineering decision, mistake and architectural trade-off while building ByteVault from scratch.
Learning GenAI using JavaScript
Learning the Generative AI in the Javascipt from 'GenAI with Javascript 2026' Cohort from chaicode.com
Lessons from Production
Interesting backend problems I solve at work, generalized without exposing company-specific implementation.
Go Learning Notes
Concurrency, Interfaces, Memory, Context,Patterns.
Docker Learning Notes
Docker, Docker Compose, Docekr bild, RUN, Images.
Get In Touch
Interested in Backend & Applied AI engineering?
Let's talk.
I'm currently working as a Backend & Applied AI Engineer building production systems with Node.js, FastAPI and modern infrastructure. If you're hiring, collaborating on an interesting project, or just want to discuss distributed systems, I'd love to connect.