AI & Geospatial Engineer
Generative AI · Geospatial Systems · Climate Intelligence
Seasoned AI & Geospatial Engineer with deep expertise in Generative AI, Machine Learning, and Satellite Data Processing. Architecting autonomous multi-agent workflows, deploying Vision Transformers, and building no-touch AI pipelines that transform terabytes of raw Earth observation data into actionable climate intelligence for global enterprises.
Core Proficiency
Agentic AI & LLM Toolchain
Local LLMs (LLaMA), MCP servers, multi-agent orchestration, no-touch AI pipelines, and enterprise inference interfaces.
ML & Simulation
Time-series forecasting, climate risk, ESG scoring, and forward-looking analytics at institutional scale.
Geospatial Technology
Earth observation, DEM/SAR fusion, multi-temporal analytics, cloud-native geospatial on AWS/GCP.
Career Arc
2026 – Present
Climate DataOps Engineer · MSCI
Agentic workflows · TCFD/EU Taxonomy · Climate data pipelines
2025 – 2026
Software & Geospatial Engineer · Encora / EOG
Vision Transformers · Methane detection · Satellite analytics
2024 – 2025
GIS Data Engineer · Valectus
CNN/U-Net · GAN super-res · Flood risk · NYU Stern
2023 – 2024
Data Engineer · Nineleaps Technologies
ETL pipelines · Big data · Tableau / Power BI
AI & Geospatial Proficiency
From autonomous LLM agents to satellite-borne Vision Transformers — a stack that spans the full distance between raw data and operational decision-making.
Working with local LLMs (LLaMA), MCP servers, autonomous agents, multi-agent orchestration, and no-touch AI pipelines. Expert in prompt engineering, agent routing, and deploying inference interfaces for enterprise workflows.
Time-series analysis, predictive modeling, Monte Carlo simulations, statistical forecasting, and simulation data proficiency. Expertise in climate risk forecasting, ESG scoring, and forward-looking analytics.
Earth observation, satellite data processing, DEM/SAR fusion, multi-temporal analytics, and cloud-native geospatial systems on AWS and GCP. Deep expertise in GIS pipelines across climate, oil & gas, and urban domains.
Building end-to-end data engineering systems across climate, ESG, and sustainability for multiple global enterprises. Experienced in TCFD and EU Taxonomy alignment, physical risk scoring, and emissions trajectory analytics.
Experience
Architecting automated, multi-agent workflows to ingest and harmonise climate and geospatial data.
Led end-to-end development of a proprietary geospatial exploration application adopted by ~70% of client staff and highlighted in EOG Resources' quarterly earnings report. Designed and trained Vision Transformer architectures on multi-spectral satellite imagery (Sentinel-2, Landsat-9, Maxar) for automated methane plume detection — achieving >92% accuracy and reducing manual inspection workflows by 60%+.
Engineered an automated GIS pipeline (Python, GDAL, GeoPandas, PostGIS) cutting 1,200+ manual hours per year and saving $120K for NYU Stern's Project Mumbai. Developed AI models — including Vision Transformers and CNN-UNet — for LULC classification; applied GANs to enhance legacy satellite imagery for remote road detection. Designed cloud-native flood risk systems using Monte Carlo simulations on GCP.
Designed and implemented ETL pipelines across 10+ diverse data sources; streamlined big data workflows improving processing speed by 35%. Built interactive dashboards in Tableau and Power BI, driving data-driven decisions for a top-3 cab service company across 50+ cities.
Selected Projects
01
Multi-agent RAG pipeline ingesting 100k+ climate datasets into a vector store for natural language querying of physical risk scores. A Bayesian inference layer quantifies uncertainty bounds; a ReAct-style agentic loop handles TCFD and EU Taxonomy compliance checks autonomously.
02
Multi-source satellite fusion pipeline using Sentinel-1 C-band SAR and Sentinel-2 optical data to classify 17+ tree species across Indian forests — combining spectral band engineering with ensemble ML for high-accuracy ecological mapping at scale.
03
Proprietary geospatial application replacing drone and on-site surveys with open-source satellite data. Automated methane plume detection via Vision Transformers on multi-temporal multi-spectral imagery — featured in EOG Resources' quarterly earnings report.
04
Research investigating the combined potential of Sentinel-1 and Sentinel-2 for automated deforestation detection, developed within IIT Indore's Earth Observation Lab. Integrated multi-temporal SAR coherence with optical NDVI time-series for change detection.
Turning raw satellite telemetry into climate intelligence — one pipeline,
one model, one decision at a time.
Technical Stack
Programming
AI & Machine Learning
GIS & Earth Observation
Data & Cloud
Education
M.Tech · Astronomy, Astrophysics & Space Engineering
GPA 8.3 / 10 · Graduated June 2023
Thesis: Potential of Sentinel-1 & Sentinel-2 for deforestation detection — Earth Observation Lab
B.E. · Electronics Engineering
GPA 7.2 / 10 · Graduated June 2021
Gold Medalist — Silverzone International Olympiad (Informatics)
Contact