Hi, I'm Nikita Gupta - GSET Data Engineer at NYU Center for K12 STEM Education
I am driven by the challenge of transforming complex data into scalable intelligence. I thrive in high-rigor environments where I can build end-to-end ML systems that move beyond theory into real-world impact.
At a glance
Curated signals on strengths, focus areas, and how they can help.
Modernized enterprise data infrastructure at Deloitte, delivering a 50% increase in system efficiency.
Currently architecting production-grade MLOps pipelines and LLM systems as a researcher at New York University.
Provides technical guidance on building scalable data platforms and implementing high-performance retrieval-augmented generation systems.
🚀 Career trajectory
Corporate Scaling
Engineered production machine learning systems for large enterprise clients at Deloitte.
✦ ML Engineer
✦ Enterprise Data
Advanced R&D
Graduated with MS in Computer Engineering while advancing AI research at NYU.
✦ Graduate Research
✦ Computer Engineering
💪🏻 Superpowers
Systemic Transformation
Modernizing legacy architectures for peak efficiency
✦ Replaced legacy SAS systems with optimized Python and PySpark workflows.
✦ Achieved 50% performance gains through rigorous architectural refactoring.
✦ Bridge the gap between data science theory and production stability.
Scalable AI Engineering
Deploying production-ready MLOps and LLM ecosystems
✦ Design end-to-end pipelines utilizing Docker, Kubernetes, and MLflow.
✦ Integrate RAG and generative AI into scalable cloud data ecosystems.
✦ Manage high-volume data lifecycle from ingestion to model serving.
Academic-Industry Synthesis
Advancing technical frontiers through research-backed application
✦ Bridge graduate-level research with industrial engineering rigor.
✦ Translate complex algorithm design into tangible enterprise value.
✦ Stay at the forefront of evolving agentic AI developments.
I'm excited about
✦ Exploring cutting-edge research collaborations with industry-leading AI labs.
✦ Connecting with peers to discuss the future of agentic workflows.
✦ Identifying unique opportunities to apply MLOps to large-scale social impact projects.
I can help with
✦ Bridge the gap between experimental research and production-grade AI infrastructure.
✦ Architect cloud-native data migrations to maximize legacy system scalability and efficiency.
I would love your help on
✦ Connect with MLOps leads building production-scale multimodal inference architectures.
✦ Join exclusive peer groups focused on responsible AI governance and ethical deployment.
✦ Find technical co-founders or investors for commercializing advanced agentic AI research.