INITIALIZING / warming the runtime
00
◇ R&D ENGINEER ◇ EST. 2002 ◇ BENGALURU, IND

Isaac A.

Building software, 01 AI, 02 and data products 03 — at the edge of research and shipping.
View selected work →
SCROLL · 2026
Current
R&D Engineer · msg group
Repos
49
Stack depth
Full-stack · Big Data · AI
ORCID
0009-0008-3123-8309
(01) ABOUT

Engineer
by trade,
researcher by instinct.

[ INDEX / 01 OF 07 ]
I'm Isaac — an Software R&D engineer shipping production systems at msg group, working across full-stack applications, big-data pipelines, and machine learning. I care about systems that are measured, reproducible, and quietly correct.

Outside of work, I Like Playing Chess & PUBG, break things on purpose, and also have an YouTube channel. My name means "one who laughs" — I try to live up to it.
Role
Engineering Consultant — Software, AI, Big Data
Based
Bengaluru, India · UTC+05:30
Currently
Machine Learning · GenAI · Apache Spark
Open to
Angular · Spring Boot · Spark · GenAI collaborations
Elsewhere
GitHub · LinkedIn · X · YouTube · Kaggle
(02) SKILLS

A stack
shaped by curiosity.

[ DRAG / HOVER / CLICK ]
(03) EXPERIENCE

Where I've
put in the hours.

[ 02 ROLES · 2023 → NOW ]
CURRENT 2024 → Present Bengaluru · Hybrid
Engineering Consultant / Software + Data
msg group
Building on an automated-driving big-data platform — ingesting, transforming, and serving terabyte-scale sensor and telemetry data that feeds downstream model training and validation. Working across Spark on Databricks, orchestrated on Azure, with hard data-quality gates and end-to-end lineage.
  • Design and ship Spark pipelines processing TB-scale automotive sensor data
  • Own data-quality, schema evolution, and lineage across the ingestion tier
  • Collaborate with engineers to cut iteration time on validation workflows
  • Explore GenAI & ML tooling for internal R&D productivity
Apache SparkDatabricksAzurePythonBig DataSpringbootADAS
Domain
Automated
Driving
Scale
Terabyte-class ingest, daily
Stack depth
Data · Platform · MLOps
2023 — 2024 Bengaluru
Research and Development Intern/ Product
Software AG · E2EM R&D Team
Worked on E2EM (End-to-End Monitoring) — Software AG's enterprise observability product — with Standard Chartered Bank as the flagship client. Shipped the Dedicated Index feature: a bespoke indexing layer that let the bank slice and query high-cardinality transaction telemetry at the speed their SRE and compliance teams actually needed.
  • Delivered the Dedicated Index feature end-to-end — design, implementation, rollout
  • Owned integration & support with team
  • Hardened E2EM's query performance on event streams
  • Partnered across backend, frontend, and SRE to ship in a regulated environment
JavaSpring BootElasticsearchAngularObservabilityEnterprise
Product
E2EM
Monitoring
Client
Standard Chartered Bank
Shipped
Dedicated Index feature
(04) SELECTED WORK

Things I've
actually shipped.

[ 03 CASE STUDIES ]
P / 001
Automated Driving, at scale
Apache SparkDatabricksAzureBig DataADASProduction
Big-data platform work at msg group for an automated-driving program — ingesting, transforming, and serving terabyte-scale sensor and telemetry data for downstream model training and validation pipelines. Spark on Databricks, orchestrated on Azure, with rigorous data-quality gates and lineage. Production workload.
Case study · on request →
TB-scale
daily ingest volume
msg group
client / employer
2024 →
ongoing
P / 002
Pothole Detection + Locating
Machine LearningComputer VisionGPSAlert SystemCivic Tech
A full pipeline that detects potholes from camera input, geotags them, and pushes alerts to authorities and drivers. Trained on custom-collected Indian road footage — the dataset I wish existed. Combines an image classifier with a location-stamping service and a lightweight dashboard for civic agencies.
View on GitHub →
3-part
detect · locate · alert
civic
intended deployment
ML
pipeline
P / 003
Predictive Maintenance
Time-SeriesPythonIndustrial IoTForecasting
A predictive-maintenance system that forecasts component failure from sensor time-series — reducing unplanned downtime on industrial machinery. Feature engineering on vibration and thermal signals, model selection across gradient-boosted trees and recurrent networks, and a deployable scoring API. The kind of problem where one false negative costs more than the model.
View on GitHub →
T-N
sensor time-series
↓ downtime
business outcome
Py
training + serving
(05) RESEARCH

Published
& indexed.

[ ORCID 0009-0008-3123-8309 ]
Pothole Detecting, Locating & Alert System Using Machine Learning
Isaac A · et al. APPLIED ML Civic Infrastructure
'23
Voice Classification & Emotion Recognition Using ML
Isaac A · Jahnvi · Irfan SPEECH / SENTIMENT Internship Research
'22
Predictive Maintenance for Industrial Systems
Isaac A TIME-SERIES / IOT Forecasting
'23
AI-Based Text-to-Image Generation — Applied Study
Isaac A GEN-AI OpenAI API
'23
(06) CREDENTIALS

Certified
in the boring stuff too.

[ 05 ACTIVE ]
◇
OCI Data Science Professional
Oracle Cloud
◇
Azure AI-900
Microsoft
◇
Azure DP-900
Microsoft · Data
◇
Azure Databricks Platform Architect
Databricks
◇
Full-Stack Specialization
Angular / React
◇
OCI RAG Vector Search Professional
Oracle
(07) CONTACT

Let's
talk.

[ RESPONSE < 24H ]
Got a hard problem?
Send it over.
isaacinfrastructure@gmail.com
GitHub LinkedIn X / Twitter YouTube Kaggle Stack Overflow HackerRank ORCID
◈ TWEAKSv1