A quick introduction — where I come from, what I've built, and why I'm equally at home in a codebase, a Jupyter notebook, a data warehouse, or a stakeholder review.
I'm an engineer who's comfortable across the stack — from backend development and full-stack web apps, to machine learning and data pipelines, to analytics and business reporting. I'm currently finishing a Master's in Computer Science at Stevens Institute of Technology in Hoboken, NJ (graduating May 2026).
My career started in software engineering. I took internships at Writin and Notebooknb in Mumbai building PHP/MySQL backends — user authentication systems, RESTful APIs, CMS platforms, and responsive frontends with HTML/CSS/JS. That foundation in shipping production code still shows up in everything I build today.
From there I went deeper into data and ML. I earned a BSc (Honours) in Information Technology specializing in Data Science from Somaiya Vidyavihar with a 9.09/10 CGPA. Alongside coursework in statistics, applied machine learning, and database systems, I interned and then worked full-time at Rajlaxmi Solutions as a Data Engineer — owning ETL/ELT pipelines for 40+ clients, dimensional modeling, REST API development for BI tooling, and migrating 200GB+ of legacy data to cloud frameworks.
At Stevens I've broadened again. I'm taking graduate courses in advanced algorithms, big data, applied ML, and generative AI — building RAG systems with LangChain, implementing MapReduce patterns in Hadoop, training classifiers in PyTorch, and architecting end-to-end streaming pipelines with Kafka and Spark. I also picked up AWS Certified Data Engineer – Associate and three Microsoft Azure Data Fundamentals certifications.
Most recently, my role at Barnes & Noble College put the analyst hat on: data-driven demand forecasting that improved inventory accuracy 30%, sales reporting, and operational workflow optimization — a good reminder that the best data work translates to decisions that real humans make.
I'm looking for roles — full-time or summer internships — where I can work across all of these: Software Engineer, AI/ML Engineer, Data Engineer, Data Scientist, Data Analyst, or Business Analyst. If it involves building something that ships and affects decisions, I'm interested.
4+ years of engineering. Comfortable with backend (Python, Node.js, PHP), REST APIs, databases, and full-stack development. The PHP/MySQL era taught me to ship; the modern stack taught me to ship well.
Production ML thinking — not just model accuracy but feature pipelines, versioning, deployment, and monitoring. Hands-on with LangChain RAG, scikit-learn classifiers, PyTorch models, and vector DBs.
1.5+ years in production — ETL/ELT, Medallion, dbt, Airflow, Snowflake. Shipped pipelines moving 500k+ records daily for 40+ clients. AWS Certified Data Engineer Associate.
BSc Honours in Data Science — trained in statistics, hypothesis testing, ML, and modeling. I can take a fuzzy business question, define the metric, build the model, and explain what it means.
SQL-native. Built BI tools and REST APIs at Rajlaxmi that contributed to an Industry Excellence Award. Defined KPIs, automated reporting workflows that saved 15+ hrs/month.
Demand forecasting that improved inventory accuracy 30% at Barnes & Noble. Comfortable translating between business stakeholders and technical teams — I've done it on both sides.
Predictable, tested, well-documented. Production code nobody has to think about at 2am is worth ten "clever" solutions that demand constant attention.
Readable code, good PR etiquette, sensible git hygiene, tests that actually run. The multiplier effect of a clean codebase compounds across a team.
The other 90% is data pipelines, feature engineering, reproducibility, deployment, and monitoring. A 95% accurate model you can't deploy is worth less than an 85% model you can.
Schema validation, null checks, anomaly detection — built in from day one, not retrofitted after the first incident. Same goes for app validation and API contracts.
The best analyst I know says "a chart you have to explain is a chart that failed." I try to make every dashboard, metric, and model output communicate clearly without a 10-minute preamble.
Columnar storage, smart partitioning, efficient algorithms, right-sized infra — the best performance gains almost always double as cost savings. Same principle across app, ML, and data work.
Currently into Designing Data-Intensive Applications by Martin Kleppmann, The Pragmatic Programmer, and anything on applied ML systems design.
Local LLMs with Ollama, vector DBs (ChromaDB, FAISS), full-stack side projects with Next.js and Supabase, and the uv Python package manager.
Where software engineering meets ML Ops — keeping models, features, and product code in sync without duplicating logic everywhere.
Full-time roles post-graduation (May 2026) and summer 2026 internships in SWE, AI/ML, Data Eng, Data Science, or Analytics — NYC metro preferred but open to relocation.