Available for work · UK & Remote

Rahul Surya

I build systems that run on supercomputers — ML pipelines, parallel algorithms, and production APIs that hold up under load.

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SPIN VELOCITY 0.00 J

The Engineer Behind
the Algorithm

Most of my work lives somewhere between ML and systems — training pipelines that actually run fast, parallel code that doesn't embarrass itself on a supercomputer, APIs that hold up under load.

Currently finishing my MSc in High Performance Computing with Data Science at the University of Edinburgh. Previously built production ML systems at ISRO's National Remote Sensing Centre — real meteorological data, real users, real constraints.

I care about the stuff underneath the model — memory, scheduling, latency. If it runs on a cluster, I've probably optimised it.

0 Industry Certifications
0 % Model Accuracy @ ISRO
0 GB Largest Dataset Processed

Tech Stack

Languages

Python C/C++ Java SQL Bash

ML / AI

PyTorch TensorFlow Hugging Face Scikit-learn

HPC

MPI OpenMP CUDA ARCHER2 Slurm

Infrastructure

Azure Docker Kubernetes CI/CD

Data

Spark PostgreSQL MongoDB Redis Kafka

What I've
Built

Live from GitHub — filtered to performance-critical systems & ML pipelines

Fetching from GitHub…

Distributed Systems
Simulation

An interactive Raft Consensus Cluster visualising elections, replication, partitions, and state recovery.

Simulation: Active
Node 1: Click to toggle offline
Leader
Candidate
Follower
Offline
[INFO] Cluster initialised with 5 nodes.
[INFO] Starting randomized election timeouts (3.5s - 5.5s)...
package raft

import "sync"

type State int
const (
    Follower State = iota
    Candidate
    Leader
)

type LogEntry struct {
    Term    int
    Command string
}

type Raft struct {
    mu        sync.Mutex
    id        int
    peers     []int
    state     State
    
    // Persistent consensus state
    currentTerm int
    votedFor    int
    log         []LogEntry
    commitIndex int
}

// AppendEntries RPC handles log replication & heartbeats
func (rf *Raft) AppendEntries(args *AppendEntriesArgs, reply *AppendEntriesReply) {
    rf.mu.Lock()
    defer rf.mu.Unlock()
    
    // Reject outdated leader terms
    if args.Term < rf.currentTerm {
        reply.Success = false
        return
    }
    
    // Reset election timer
    rf.heartbeatCh <- true
    
    // Truncate logs if conflict exists & append new entries
    if len(args.Entries) > 0 {
        rf.log = append(rf.log[:args.PrevLogIndex+1], args.Entries...)
    }
    
    // Commit and apply to state machine
    if args.LeaderCommit > rf.commitIndex {
        rf.commitIndex = min(args.LeaderCommit, len(rf.log)-1)
    }
    reply.Success = true
}

Live Skill Profile

Pulled from GitHub in real time — updates as I push new code

My GitHub Profile Typical ML Engineer Role

Scores derived from public GitHub repositories. Reference benchmarks based on common ML Engineer job requirements.

Where I've
Worked

MSc Student — High Performance Computing with Data Science

University of Edinburgh · EPCC

2025 – 2026

Specialising in data science, parallel programming on ARCHER2 (UK National Supercomputer), MPI/OpenMP, GPU computing, and large-scale machine learning systems.

MPIOpenMPCUDAARCHER2Data Science

ML Engineer Intern

ISRO — National Remote Sensing Centre

Oct 2023 – Jan 2024

Built a ConvLSTM lightning prediction system end-to-end in PyTorch. 92% accuracy on live data, deployed to active meteorologist workflows. Cut training time 35% by optimising on 500 GB WRF datasets with VAE. Also built the React dashboard they used to monitor it.

PyTorchConvLSTMReactProduction ML

Data Science Intern

Clustor Computing

Jun 2023 – Sep 2023

Stock price models with 94% directional accuracy. Built data pipelines and Tableau dashboards for investment teams.

Data ScienceTableauFinancial ML

Software Engineer Intern

Develoscope Software Solutions

Jun 2023 – Sep 2023

Four Java web applications shipped from scratch to production. JSP, Tomcat, full-stack development.

JavaJSPTomcatFull-Stack

Credentials &
Certifications

Let's Build
Something

Looking for a developer who can handle ML systems, HPC workloads, and production APIs? I'm your person. Currently based in Edinburgh, available for remote and UK-based roles.

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