Technology Developer Intern, Barclays June 2026 – August 2026
Education
B.Tech in Artificial Intelligence & Data Science Aug 2023 – Jul 2027 · CGPA 9.71
How to read the orrery
Orbit radius is months elapsed since the degree programme began; each body carries a moon for up to six of the technologies it was built with. A planet's hue is its domain, its surface is how far the work got, and a ring marks work that is not solely mine. CivicLens is drawn unfilled because it records no measurements at all. Body size is an ordinal ranking of prominence, not a measured magnitude. The project metrics are not commensurable with one another.
§1Experience
Experience
Backend platforms, product workflows, and analytics infrastructure across three internships.
June 2026 – August 2026
Barclays
Technology Developer Intern
Helped develop a sanctions-screening proof of concept into a configurable platform, covering processing pipelines, system design, and operational tools.
Contributions and tools at Barclays
Participated in system-design proposals with engineering and project stakeholders, translating operational requirements into architecture and workflow.
Built and iteratively evolved a Python, FastAPI, React, and TypeScript platform capable of processing files with 80M+ records across DEV, UAT, and pre-production environments.
Public copy is limited to résumé-level scope and does not imply Barclays endorsement.
September 2025 – November 2025
Makeflow India
Backend Developer Intern
Refactored a Node.js backend for an AI counselling product, separating inference from session state and simplifying a four-stage conversational workflow.
Contributions and tools at Makeflow India
Reduced inter-service coupling by approximately 40% and code redundancy by approximately 30%.
Implemented keyword-triggered crisis routing with a routing decision under 100 ms.
Built a profile-aware returning-user workflow that removed redundant onboarding for approximately 60% of repeat sessions.
Node.js
JavaScript
REST APIs
Scope: backend routing and product workflow engineering, not clinical decision-making.
December 2024 – January 2025
Segmentriq Analytics
Data Analytics Intern
Brought operational data into a SQL warehouse and built Power BI reporting for revenue, churn, and sales-funnel analysis.
Contributions and tools at Segmentriq Analytics
Integrated 5+ source systems into a normalized, fact-and-dimension data warehouse.
Delivered 3+ Power BI dashboards with 15+ custom DAX measures for revenue, churn, and funnel analysis.
Reduced ad-hoc reporting turnaround by approximately 35% and improved refresh time by approximately 25% through query and model optimization.
SQL
ETL
Data Warehousing
Power BI
DAX
§2Selected work
Selected projects
What I built, what the evaluation showed, and the trade-offs behind each system. Open a case study for the full engineering story.
Evidence-grounded incident investigation for distributed systems
A local incident-investigation platform that brings metrics, logs, traces, service topology, and changes together to explain likely causes with supporting evidence.
Model gate
The deterministic baseline beat the learned ranker, so the model remained ineligible for promotion.
Figure 2.1Shows the incident path from OpenTelemetry input through time and topology scoping to correlation and an evidence-cited diagnosis. The incident identifier and its seven linked signals are an illustrative walkthrough, not a recorded incident, and no latency or accuracy figures appear.
Decision
Replay immutable evidence
Approach
Store checksum-addressed telemetry bundles in MinIO and perform RCA against replayed artifacts rather than querying live systems during analysis.
Trade-off
Collection failures and missing signals stay visible; the system does not silently backfill them from a later live state.
A research system that compares four specialised AI agents with simpler rules for ranking Indian mutual funds. Includes reproducible evaluation and an inspection dashboard.
Evaluation finding
The persisted study found a directional, but not statistically significant, advantage over a simple baseline.
Figure 2.2Shows the full flow from public sources through normalisation, the feature engine, a four-way agent fan-out and fan-in, deterministic consensus, evaluation, and the dashboard. The counts it carries (20+ engineered metrics, six dashboard tabs) describe the system's shape. No measured scores, timings, or throughput appear.
Decision
Separate evidence from model judgement
Approach
Engineer return, risk, cost, and consistency objectives before asking four role-specialised agents to rank candidates.
Trade-off
The role prompts are deliberately prescriptive, so the agents are closer to specialised rerankers than unconstrained analysts.
An academic collaboration using Kafka and Spark to score transaction streams. A simulated local benchmark tests whether fewer features can reduce inference cost.
Measured trade-off
The three-feature model retained most AUC-ROC, but produced materially more false positives than the full-feature baseline.
Architecture & trade-offs for Real-Time Fraud Detection Pipeline
Figure 2.3Shows the Kafka to Spark to inference path together with the benchmark's own measured values: 3,285 events per second, a 26.53 ms median batch, 99.2% AUC retained on a reduced feature set, 284K+ transactions, and 577:1 class imbalance. Every figure comes from a simulated local streaming benchmark, not production traffic.
Decision
Optimize a compact feature subset
Approach
Use a Quantum-Inspired Evolutionary Algorithm with a Random Forest proxy and a parsimony penalty to select three of 30 features.
Trade-off
The three-feature Random Forest produces far more false positives than the 30-feature baseline: 466 versus 27 on the held-out test set.
From the API a product calls to the data and models behind it.
Backend systems
APIs, authentication, failure handling, and tools for operating services.
FastAPI
Spring Boot
Node.js
PostgreSQL
Redis
Data pipelines
Batch and streaming workflows with traceable data and measurable performance.
Kafka
Spark
PySpark
Pandas
Warehousing
Applied AI
ML and language-model workflows tested against simpler baselines.
PyTorch
TensorFlow
MLflow
Spark MLlib
LLM systems
How these technologies work together
Systems→Kafka→Data
Carries durable event streams between services and data processing.
Systems→FastAPI→Product
Exposes backend workflows through typed, product-facing APIs.
Data→PyTorch→Intelligence
Turns prepared data into trainable and evaluable model behavior.
Systems→PostgreSQL→Data
Provides transactional storage for data-intensive services.
§4Background
A little more about me
I am studying Artificial Intelligence & Data Science in Mumbai, with graduation expected in July 2027. My interests sit between backend engineering, data infrastructure, and applied AI.
I’m interested in what makes a system useful beyond its demo: how it handles failure, how its decisions can be reviewed, and whether a simpler approach works better. My projects make those questions visible through tests, baselines, and honest limitations.
Institution
K.J. Somaiya School of Engineering
Programme
B.Tech in Artificial Intelligence & Data Science
Period
Aug 2023 – Jul 2027 · In progress
CGPA
9.71
Let’s build something people can rely on.
I’m open to 2027 new-grad software engineering roles across backend systems, data infrastructure, and applied AI. Email me to start a conversation.
