Shape the data first
Clean contracts, clear lineage, and resilient processing create the foundation for everything that follows.
Data engineering · cloud · applied AI
Building data systems with clarity and intelligence.
I am Lokesh Jasti, a data engineer and AI builder designing dependable platforms that turn complex information into useful, human-centered products.
Based in the United States · Open to meaningful engineering collaborations
Current focus
Private, adaptive AI systems
Data platformsReliable by design
Applied AIEvidence before spectacle
Cloud systemsBuilt to evolve
How I work
Engineering with a point of view.
I work across data pipelines, analytics, cloud infrastructure, and AI interfaces. The goal is not more technology. It is a system people can understand, trust, and use.
Make AI accountable
Useful AI should show its evidence, respect privacy, and make uncertainty visible rather than hiding it.
Design for decisions
Dashboards and workflows should reduce cognitive load and help people move from insight to action.
Personal projects
Ideas becoming real systems.
A working studio of private AI, analytics, health intelligence, voice technology, and evidence-led software. Select any project to step inside.
Financial intelligence↗
FinSight AI
An adaptive financial analysis workspace that transforms uploaded spreadsheets and documents into interactive analytics, cited answers, and decision-ready intelligence.
Health intelligence · Upcoming↗
Personalized Health AI
A privacy-aware health intelligence concept combining graph relationships, federated learning, and patient-centered insights.
Document intelligence↗
Vastavik AI
An evidence-led PDF originality workspace for transparent document comparison and structured reporting.
Private voice AI↗
Private Voice Translator
Offline voice-to-voice translation built for private conversations without depending on a cloud service.
Experimental system↗
Local Desktop Intelligence
An experimental local voice workflow connecting speech, private reasoning, and everyday desktop actions.
Academic projects
Coursework transformed into systems.
Cloud engineering, distributed analytics, environmental intelligence, NLP, and visualization projects developed through graduate and undergraduate study.
Cloud engineering capstone↗
AWS Fisheries Data Pipeline
A cloud-native analytical platform transforming heterogeneous global fisheries records into efficient, explorable datasets.
Streaming analytics↗
Spark Air Quality Forecasting
Real-time and historical environmental analytics for detecting patterns, forecasting change, and identifying unusual events.
Distributed NLP↗
Sentiment Through Time
A Hadoop pipeline uncovering linguistic and emotional change across historical literature.
Information visualization↗
U.S. Air Quality Atlas
An interactive geospatial dashboard connecting national pollution patterns with state-level detail.
Distributed systems lab↗
Spark Word Count Lab
A complete distributed Spark processing workflow paired with a lightweight web interface.
Technical range
From raw data to useful product.
I connect engineering depth with product thinking across ingestion, transformation, storage, analytics, machine learning, deployment, and interface design.
PythonSQLApache Spark
DatabricksAirflowKafka
AWSAzureSnowflake
FastAPINext.jsDocker
PostgreSQLNeo4jPower BI
Machine LearningNLPData Visualization
ETL ArchitectureData QualityCloud Automation
Experience
Work shaped by real systems.
My experience spans enterprise data engineering, cloud migration, analytics enablement, and production workflows. Open a role to see the work behind the title.
- Developing dependable data workflows that connect operational sources with analytics-ready models.
- Strengthening observability, validation, and data quality so teams can trust what reaches reporting layers.
- Working across engineering and business teams to turn evolving requirements into maintainable solutions.
- Built and supported cloud data pipelines for operational and analytical use cases.
- Translated complex source data into reusable models for dashboards, reporting, and downstream analysis.
- Improved pipeline reliability through validation, orchestration, monitoring, and clear ownership.
- Designed ETL workflows that brought together structured data from multiple enterprise systems.
- Supported cloud modernization and helped shape scalable processing patterns for recurring workloads.
- Partnered with analysts and stakeholders to make datasets easier to discover, understand, and use.
- Learned production data engineering through pipeline development, SQL modeling, testing, and documentation.
- Contributed to repeatable data preparation and reporting workflows under experienced engineering teams.
Published research
Applied AI, documented.
Research exploring how computer vision and deep learning can solve practical problems in agriculture and document processing.
IEEE · ICEARS 2023
Read publication
Early Diagnosis of Folium Disease in Solanum Lycopersicum using CNN
A convolutional neural network approach for recognizing foliar disease patterns in tomato plants and supporting earlier intervention.
IEEE · ICIPTM 2023
Read publication
Handwritten Text Extraction from Application Forms
An image-processing and deep-learning workflow for locating and extracting handwritten content from structured forms.
Education
Computer science, deeply practiced.
A foundation spanning algorithms, data systems, networking, artificial intelligence, visualization, software design, and cloud computing.
University of North Carolina at Charlotte
Computer Science · Completed May 2025
Explore coursework ↗V R Siddhartha Engineering College
Computer Science and Engineering
Explore coursework ↗
Let us build something useful
Have a complex data problem or an ambitious AI idea?
I enjoy work where engineering quality, intelligent automation, and thoughtful user experience have to coexist.