Available for senior & staff backend roles Bengaluru → Barcelona

Code that 200,000 network devices depend on. AI agents that handle a million requests a day.

Five years building backend systems, from kernel-level networking at VMware to AI-agentic infrastructure at a fast-moving startup. Currently relocating from Bengaluru to Barcelona for senior and staff backend roles.

Download resume View work
0M+
API requests / day
Knowl · hybrid AWS + GKE
0K+
Edge devices served
VMware · VeloCloud SD-WAN
+0%
AI accuracy lift
Language detection agent
0%
Test coverage
Up from 5% · legacy C codebase
01 About

Backend engineer for problems with real constraints.

I like problems where the constraints are real: limited memory, strict latency budgets, traffic that won't wait for you to scale up. Day to day, that means owning technical decisions, defining API contracts, and mentoring the engineers around me, as much as it means writing code.

Most recently, I was a founding engineer at Knowl, where I built the Python and FastAPI backbone for an AI-native product handling over a million requests a day, including an autonomous agentic system that improved language detection accuracy by 45%.

Before that, I spent four years at VMware on the VeloCloud SD-WAN platform, writing production C and C++ for systems running on 200,000+ edge devices: threat detection, IPv6 prefix delegation, and a testing framework that took a legacy C codebase from 5% to 90% coverage.

I studied Computer Science at IIT Guwahati, and still keep my competitive programming chops sharp. Outside of work, I run local LLMs on a Mac Mini and write about what I find.

02 Experience

Where I've shipped

Nov 2025 – Jan 2026
Knowl
Bengaluru, India

Founding Engineer

  • Designed Python/FastAPI backend services handling 1M+ API requests/day on a hybrid AWS EC2 and GKE architecture.
  • Built an autonomous AI agentic system for language detection using LLM reasoning and multi-strategy fallback, improving accuracy by 45% through prompt engineering.
  • Implemented Redis-based rate limiting and throttling, keeping high-volume APIs stable under P95 load.
  • Built scalable REST APIs with Pydantic validation across a reactive microservice architecture.
Jul 2021 – Oct 2025
VMware · Arista
VeloCloud SD-WAN · Bengaluru, India

Member of Technical Staff 2

  • Built a network interface tracking system giving real-time visibility into 200,000+ edge devices, from kernel-level data extraction to the user-facing dashboard.
  • Delivered Advanced Threat Protection (IDS/IPS, threat intelligence, URL filtering) that mitigated 10,000+ malicious connection attempts a month.
  • Designed and implemented IPv6 DHCP Prefix Delegation for dynamic prefix allocation across large SD-WAN deployments with zero-NAT global addressing.
  • Led the introduction of a unit testing framework for legacy C code, scaling coverage from 5% to 90% and unlocking full CI automation.
  • Built a packet processing latency visualization platform that sped up performance debugging.
03 Projects

Things I build outside of work

pytest-tenantguard

Building

A pytest plugin and AST linter that catches cross-tenant data leaks in multi-tenant SaaS codebases before they ship. MIT-licensed core, with a paid compliance dashboard for teams that need audit trails.

Python pytest AST

MigrationGuard

Building

A CLI tool that catches backward-incompatible database migrations before they break a zero-downtime deploy, by tracing migration diffs back to the application code that actually depends on them.

Python CLI PostgreSQL

Interview Helper

Active

A local RAG application that stores and queries interview experiences with semantic search and metadata filtering, running entirely on local infrastructure end to end: ingestion, indexing, retrieval, and LLM-powered answers.

Python Ollama ChromaDB

Librarian

Active

A multi-book conversational AI that ingests PDFs, builds a searchable knowledge base, and answers questions with citations, using hybrid retrieval across vector search, TF-IDF, and cosine similarity.

Python RAG Hybrid Search

More on GitHub →

04 Field notes

Notes from building with local AI

Local AI · Systems

The real bottleneck in local AI isn't model size. It's memory.

Running larger coding models on a 16GB Mac Mini, I noticed generation slowing down and the model losing track of earlier context as a project grew. The cause wasn't the model, it was the KV cache eating into available memory as context filled up. A smaller model with more headroom for context can outperform a bigger one on real projects, and I think the next real unlock for local AI is better memory architecture, not bigger weights.

Read the full post on LinkedIn
Benchmark · Apple Silicon

Benchmarking Qwen3.5 on a Mac Mini against my Claude subscription

Same coding prompt, run across 4B and 9B Qwen3.5 models with and without Apple's MLX backend. Runtime optimization turned out to matter almost as much as model size.

No MLX
13.13 tok/s
MLX
27.71 tok/s

~2.1x faster generation on the 4B model, with total inference time dropping from 4m39s to 1m43s. 4B models now feel genuinely usable for real engineering work.

Read the full post on LinkedIn
05 Stack

What I build with

Languages
PythonTypeScriptCC++Java
Cloud & Infra
AWS (EC2, S3, SQS)GKETerraformDockerKubernetesKEDA
AI & LLM
AI Agent DevRAGPrompt EngineeringLangChain
Frameworks
FastAPIFlaskNode.jsExpress.jsSpring
Data
PostgreSQLMySQLRedisMongoDBChromaDBElasticsearch
Practices
CI/CDAgileUnit TestingMicroservicesTCP/IPNetwork Security
Retrieval Augmented Generation (RAG)
April 2026 · Present
Claude Code: An Agentic Coding Assistant
May 2026
AWS Storage
June 2025
06 Contact

Open to senior and staff backend roles in Barcelona and across Europe.

I'm relocating from Bengaluru and ready to start the visa sponsorship process. If you're hiring backend or AI-systems engineers, I'd love to talk.

ajaiswal9043@gmail.com
Email ajaiswal9043@gmail.com LinkedIn in/abhijaiswal18 GitHub ajaiswal1818