MS AI | Machine Learning Engineer | Former SWE @BNP PARIBAS
I’m a Software Engineer with 5+ years of experience building scalable Python applications, ETL pipelines, and machine learning systems across finance, AI, and analytics.
Squark is a leading no-code AI as a Service (AIaaS) platform, empowering data-driven businesses to create actionable insights, drive smarter decisions, innovate with their data. From predictive AI to generative AI to NLP and more, Squark enables creators to harness the full potential of AI for data analysis, decision prioritization and strategic action.
Our platform is designed for ease of use, scalability, accuracy, and robust security, making creativity with advanced AI accessible to both business and technical users without the need for deep data science expertise. Just bring your ideas, and Squark will handle the rest.
Contributed to AI content generation system using Spring Boot, integrating OpenAI's ChatGPT API to dynamically generate storylines, character dialogues and scene descriptions for a graphic novel application.
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Built RESTful endpoints further to coordinate with the DALL-E API & experiment with MidJourney Discord API for automated AI-driven image generation, ensuring seamless communication between components.
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Implemented robust backend logic in Java to manage prompt creation, response handling and data flow across multiple AI services.
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Implemented data quality control measures to ensure consistency in narrative flow, stylistic coherence, and factual accuracy across the generated content.
Mentored students in DS3000 Foundation of Data Science course under Professor Sophine Clachar, PhD.
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Responsibilities involve conducting regular office hours to help students with conceptual understanding, good design practices, and debugging & grading labs, assignments in a timely manner & providing constructive feedback to improve.
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Topics included : Python fundamentals, Data Wrangling, Statistical Modeling, Hypothesis Testing and Machine Learning Pipeline & Hyper-parameter tuning.
Engineered scalable ETL pipelines in Python using Pandas, Paramiko, and Boto3 to ingest and preprocess high-volume data from seven external vendors, supporting downstream analytics across three business units.
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Standardized data ingestion for XML, CSV, and JSON formats, embedding schema validation and error-loggingroutines that ensured structural integrity across eighteen active workflows.
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Automated file transfers between on-premise Linux servers and AWS S3 buckets using Cron jobs and SSH-based scripts, saving over 24 hours of manual work weekly.
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Reduced batch job runtimes by integrating multiprocessing, optimizing I/O operations, and implementing asynchronous processing across forty scheduled data pipelines.
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Developed reusable Python modules for data type checks, normalization, and exception handling, enabling five engineering teams to unify data processing standards across projects.
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Collaborated with QA and DevOps to perform end-to-end UAT, version control with Bitbucket, and continuous deployment via Jenkins, ensuring smooth delivery of production-ready ETL assets.
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Left BNP Paribas to pursue MS however plans got delayed due to Covid-19, meanwhile took up this role.
