Santhosh Muthukrishnan
Software Engineer — AI & Cloud Pipelines
Do you use data in your business? I can help!
I am a data-focused backend engineer with two years of experience building cloud-native systems and data infrastructure, including serverless pipelines on AWS Lambda, SAM, SQS and SNS. I also have research experience building data and modelling pipelines for LLMs and vision-language models on Slurm-powered HPC clusters.
Currently looking for data engineering, agentic AI and LLM projects worth building.
Stats
- Lambda speed-up
- 12x
- Compute cost
- −60%
- Cloud billing, weighted avg
- −2%
- Best Gleason accuracy
- 82.56%
- Models benchmarked
- 12
- MSc result
- 81.8%
- Years in industry
- 2
- Designed multi-source data ingestion pipelines on a GPU-accelerated HPC cluster, using Slurm for scheduling, queuing and resource allocation across parallel training runs.
- Built preprocessing for multi-GB Whole Slide Images, with patching strategies that make gigapixel inputs tractable for training.
- Benchmarked 8 transformer models and 4 vision-language models across 3 datasets; the best reached 82.56% accuracy on patch-wise prostate cancer Gleason score classification.
- Extended the research pipeline into a diagnostic web app where users upload WSI/patch inputs and pick from 12 trained models.
- Engineered a reproducible experiment framework — data and library versioning, config management, logging — for rapid iteration.
- Built multimodal pipelines fusing tabular clinical data with 3D MRI imaging, including 3D → 2D conversions to fit model input constraints.
- Engineered around real-world clinical data problems: sparse longitudinal records, missing values, misaligned timestamps and schema mismatches.
- Built and validated an XGBoost pipeline end to end, reaching R² = 66.4%.
- Used auxiliary language models to generate structured annotations at scale and fed them into downstream training.
- Optimised PDF-processing Lambda workloads by up to 12x, eliminating 15-minute timeouts and cutting compute costs by 60%.
- Owned backend microservices and cloud applications in an AI-powered Intelligent Document Processing product for PDF accessibility.
- Built APIs (Spring Boot, Flask) and serverless infrastructure (Lambda, SAM, SNS, SQS) for dataset enumeration and model inference.
- Built the annotation tooling clients used to produce labelled image–JSON pairs for training document-structure models.
- Automated internal workarounds that lifted ticket activity and closure by 30%.
Cloud-Native PDF Asset Extraction System
- AWS Lambda
- Amazon S3
- DynamoDB
- REST APIs
- Python
- Java
An event-driven AWS pipeline for extracting images, captions and metadata from PDFs — Lambda for processing, DynamoDB for text, S3 for assets — built for large-scale ingestion, indexing and retrieval.
Discontinued
Properly accessible PDFs with good image captions are now very hard to find, which left the pipeline without the source data it was designed for.
OpenCodeReasoning (debugging focus)
A challenge to improve an LLM's coding and logical reasoning using NVIDIA's OpenCodeReasoning dataset — coding problems, buggy code and critiques — to help the model catch and fix bugs.
Discontinued
A loosely specified challenge built on LLM-generated critiques and code. There was no reliable way to measure accuracy, so I couldn't tell whether anything was actually improving.
- Data infrastructure
- AWS Lambda
- SAM
- SNS
- SQS
- Event-driven processing
- Dataset & annotation tooling
- Batch & async workflows
- Databases
- PostgreSQL
- MongoDB
- DynamoDB
- Redis
- Cloud & DevOps
- AWS (Serverless, EC2, S3, RDS)
- GCP
- Git
- Docker
- Kubernetes
- Jenkins
- GitHub Actions
- Backend
- Java
- Spring Boot
- Spring MVC
- Python
- Flask
- Node.js
- Microservices
- JWT
- Hibernate
- AI / ML
- NLP
- LLMs & VLMs
- Slurm HPC
- Hugging Face
- RAG
- LangChain
- Data analysis
- R
- MATLAB
- PostGIS
- scikit-learn
- pandas
- NumPy
- Testing
- JUnit
- Mockito
- Selenium
- Cypress
MSc Data Science and Analytics
2024 – 2025
Maynooth University · Maynooth, Ireland
First-class honours (81.8%)
Machine Learning, Statistical Methods, Geospatial Analysis; thesis on vision-language models in medical settings.
BTech Computer Science and Engineering
2018 – 2022
Vellore Institute of Technology · Vellore, India
First class (87.5%)
Software Engineering, Data Structures and Algorithms, Data Analytics, Cloud Computing.