Professional Experience

2+ years of professional experience in backend engineering and applied AI (LLMs, RAG, agentic workflows)

Artificial Intelligence Engineer — RSM Netherlands

September 2026 – Present | Hoofddorp, Netherlands

Part-time AI engineering role alongside my Master's, building tools for RSM's tax and ESG teams.

Proactive Research Platform: Developing an AI platform that keeps tax and ESG teams current on client-relevant developments, drafts slide decks and talking points, and supports regulation deep dives. It saves users up to 10 hours a week.
World Model: Building an evolving world model that simulates scenarios and persona-led expert discussions, feeding a continuous agentic dashboard of what world events mean for users and their clients.

Agentic AI LLMs Python

Artificial Intelligence Engineer — ABN AMRO (via AISO)

March 2026 – June 2026 | Amsterdam, Netherlands

Selected as 1 of 7 from 100+ applicants for the ABN AMRO x AI Projects programme, working with the bank's Content Design team on AURORA.

AURORA: A seven-stage AI governance and grounding pipeline that turns vague requests into evidence-based, on-brand Dutch and English article drafts. Drafts are grounded in approved sources and checked against 100+ content-quality KPIs.
My Contribution: Designed the system architecture and built the intent-classification and profile-routing stages, which sort requests by task type, language and editorial profile. I presented the working proof of concept to the Head of Marketing.

LLMs Agentic Workflows System Design

Software Development Engineer I — CloudSEK

December 2022 – June 2025 | Bengaluru, India

CloudSEK is a cybersecurity company that monitors the internet for threats targeting businesses. I worked on AI-powered tools that helped the platform make sense of massive amounts of threat data — faster and more accurately than before.

ThreatXpose: Built, with my team, a feature now integrated across all events on the XVigil platform. For every threat event it produces a "who, what and how" breakdown in about 15 seconds: the threat actors behind it, the assets or endpoints targeted, and the tactics, techniques and procedures used. A real-time threat graph and summary are generated in 3–5 seconds by pipelines that extract the relevant fields from hundreds of metadata fields and pass them to LLMs.
Threat Summarisation at Scale: Built a system that automatically reads and summarises hundreds of thousands of security alerts every day, surfacing the most important insights to analysts in under 3 seconds. This cut down the time analysts spent sifting through data by 40%.
Spotting Fake Apps: Improved a tool that detects counterfeit mobile apps impersonating real brands — the kind that steal user credentials or spread malware. After the revamp, it correctly caught 92 out of 100 fakes.
Smarter Asset Monitoring: Built a pipeline that automatically checks whether suspicious online assets (like lookalike websites or stolen logos) belong to a client's brand — work that previously required a human to do manually. This saved the team 14 hours of manual effort every week.
Data Breach Detection: Created a model that identifies when a client's internal systems or employee accounts show up in data breaches leaked on the dark web, with 93% accuracy. This let the team generate detailed, actionable breach reports for clients.
Data Indexing Tool: Wrote a command-line tool that automatically processes and organises 100GB of raw cyber data daily, halving the manual work previously needed to keep the data pipeline running cleanly.

Python Go LLMs RAG Langchain Elasticsearch

Software Developer Intern — WorkIndia

May 2022 – August 2022

Built 5+ features with Django and ReactJS, shipped 2 critical bug fixes and documented the processes.
Set up staging infrastructure with Jenkins and used Kubernetes to monitor 10+ microservices for end-to-end testing.
Built a Kafka-based logging pipeline and used Grafana for log analysis to make debugging easier.

Django React Kafka Kubernetes

Software Engineering Intern — Vidyo.ai

January 2022 – April 2022 | Bengaluru, India

Vidyo.ai is a platform that automatically repurposes long-form video content into short clips for social media. I worked on the backend infrastructure that handles video uploads and processing.

Restructured core backend services to handle more users at the same time without slowing down.
Moved video storage and processing to the cloud, making the system more reliable and easier to scale.
These changes made the platform noticeably faster — API response times dropped by 30% and video uploads completed 67% quicker.

Python FastAPI AWS Docker

Machine Learning Intern — Solus.ai

June 2021 – August 2021 | Remote

Solus.ai builds recommendation systems — the kind that suggest products or content tailored to each user. I worked on making their core engine significantly faster.

Rewrote performance-critical parts of the recommendation engine in Julia, a language better suited for heavy number-crunching than the original codebase.
The result: the system ran 60% faster on over a million data points, leading to quicker and more responsive recommendations for end users.

Julia Machine Learning Distributed Computing

Community Manager — GDSC-VIT

Vellore, India

Ran the Google Developer Student Club at Vellore Institute of Technology — a student community focused on learning and building with technology.

Organised workshops, hackathons, and events that gave students hands-on experience with real-world tech.
Mentored junior students on backend projects, helping them go from idea to working product.
Built a collaborative environment where students could learn from each other, not just from lectures.

Leadership Mentorship Event Organization

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