Career

Journey & Growth

Deep technical impact across AI research and engineering.

Work

Professional Experience

Diversified Automation

Software Engineer

Diversified Automation

Mar 2025 – Present

Santa Clara, CA, USA

  • •Multi-tenant search infrastructure: Designed multi-tenant vector search service (Qdrant) with namespace-level isolation across 100+ independent environments; collection filters enforced strict data boundaries, reducing memory overhead 80% vs. per-tenant database approach.
  • •Hybrid search service: Built search service combining inverted-index keyword matching with semantic vector retrieval to handle exact alphanumeric codes and natural language in a unified pipeline; improved Recall@5 by up to 20% over vector-only architecture with no latency regression.
  • •Low-latency query routing layer: Engineered intent-classification service that routes requests before triggering expensive downstream calls; fast-path circuit breaker for direct-lookup queries reduced p99 retrieval latency from 150ms to <5ms (30x), eliminating vector search overhead for targeted file queries.
  • •Output validation service: Built post-generation validation microservice that fuzzy-matches LLM output against an indexed SQL tag database before rendering; achieved >99% redaction accuracy of fabricated hardware identifiers.
  • •Deployment infrastructure: Owned end-to-end CI/CD pipeline (GitHub Actions + Docker + AWS) for AI microservices; automated testing, container builds, and blue-green deployments enabling zero-downtime.
PythonJavaPLC ProgrammingPandasNumPy
BMR Infotek

Data Scientist

BMR Infotek

Aug 2024 – Mar 2025

Dublin, CA, USA

  • •Large-scale text processing pipeline: Built batch embedding pipeline processing 1M+ customer records using sentence transformer models; outputs fed a segmentation service enabling precision behavioral targeting that improved marketing ROI by 20%.
  • •Distributed data ingestion service: Designed Apache Airflow DAG-based ETL system on AWS EC2 ingesting and transforming 3.5M+ records/month into Redshift; retry orchestration and task-level fault isolation reduced pipeline processing latency by 35%.
  • •Model serving & versioning platform: Built orchestration service connecting forecasting model outputs to downstream consumers; CI/CD pipelines automated model versioning, artifact promotion, and rollback, enabling zero-downtime model updates in production.
Machine LearningPowerBISQLDeep LearningRFM Modeling
Kelley School of Business, Indiana University

Data Scientist · Graduate Research Assistant

Kelley School of Business, Indiana University

Dec 2022 – May 2024

Bloomington, IN, USA

  • •Large-scale NLP & classification: Applied fine-tuned BERT models to a 100M+ row dataset for political campaign and sentiment classification, achieving 91% test precision.
  • •LLM-based analytics: Implemented LLM pipeline to analyze customer satisfaction and brand equity signals for large US companies; improved binary classification predictive accuracy by 15% and surfaced quantifiable financial market impact.
  • •Behavioral modeling: Developed and deployed a Cross-Classified Multilevel Model to predict user performance from behavioral data; uncovered key interaction patterns that improved decision-making efficiency and boosted system performance by 35%.
  • •Led research projects on television advertising, social media, and political marketing under Prof. Beth Fossen and Prof. Lopo Rego.
NLPSeabornFeature EngineeringMachine LearningExcel
Twin Cities Innovation Alliance

Data Scientist Intern

Twin Cities Innovation Alliance

Sep 2023 – Dec 2023

Minneapolis, MN, USA

  • •A/B testing infrastructure: Built traffic-routing framework across application variants; SQL-based conversion analysis drove a 17% lift in user engagement.
  • •Recommendation engine: Engineered collaborative + content-based filtering backend, optimizing preference retrieval queries to achieve 20% engagement increase.
  • •API performance: Developed Node.js RESTful APIs connecting frontends to ML microservices, cutting API response latency by 40% and driving 15% growth in daily active usage.
Statistical ModelingA/B TestingSQLRecommendation Systems
Moonplexus Pvt. Ltd.

Data Engineer

Moonplexus Pvt. Ltd.

May 2021 – Aug 2022

Pune, MH, India

  • •Healthcare platform: Designed microservices web platform (Java, Django, AngularJS, SQL) automating health information management; 40% reduction in query execution time through relational DB optimization.
  • •Cloud infrastructure: Migrated and maintained AWS EC2/RDS infrastructure, building fault-tolerant ETL pipelines with high availability for large-scale medical data storage.
  • •Deep learning deployment: Deployed TensorFlow/CNN diagnostic models via Docker with FastAPI endpoints, reducing inference latency by 30% and improving system uptime.
TensorFlowAWS EC2 / RDSCNNNode.jsETL
Indian Institute of Technology (IIT)

Machine Learning Intern

Indian Institute of Technology (IIT)

Oct 2020 – Apr 2021

Guwahati, AS, India

  • •Algorithm optimization: Researched and developed incremental/decremental versions of DBSCAN and MBSCAN algorithms in Python and C++ for distance-based outlier mining.
  • •Clustering performance: Improved computational speed by 30% and precision by 20% in clustering algorithms; tested optimizations on a fintech application with 1M+ users for customer segmentation and portfolio optimization.
PythonC++ClusteringData AnalysisMachine Learning
Academics

Education

Indiana University Bloomington

Master of Science, Data Science

Indiana University Bloomington

Aug 2022 – May 2024

Key Coursework: Applied Machine Learning, Elements of AI, Probability & Statistics, Big Data, Data Warehousing & Mining, Business Intelligence.

MIT World Peace University

Bachelor of Technology, Computer Science & Engineering

MIT World Peace University

Jul 2018 – Jun 2022

Key Coursework: Data Warehousing & Mining, Business Intelligence, Big Data Analysis, Financial Econometrics, Design & Analysis of Algorithms, Database Management.