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Moirangmayum Ronaldo

@moirangmayumronaldo

Data Engineer at Crimson Energy Experts building maritime analytics pipelines that cut 10K-row job latency to 5 seconds.

India

Message

At Crimson Energy Experts Pvt Ltd, I work on DeepDarshak, an AI-driven maritime analytics platform built for the Indian Navy. I re-architected a ClickHouse ETL pipeline that cut 10K-row job latency from 5–6 minutes to 5 seconds, and designed an AIS analytics layer spanning 13 virtual datasets and five Superset dashboards.

Previously, at Agentiwise AI Agency, I designed and developed 70+ LLM-based applications, including RAG agents, and built backend APIs with FastAPI. At Reveal HealthTech, 2070 Health, I designed an automated ELT pipeline that improved reporting speed by over 40% across two healthcare startups. I also built a personal SaaS project for AI-powered resume tailoring and proposal generation.

Experience

Work history, roles, and key accomplishments

Current

Data Engineer

Current

Crimson Energy Experts Pvt Ltd

Dec 2025 - Present (10 months)

Core data engineer on DeepDarshak, an AI-driven maritime analytics platform for the Indian Navy. Designed and maintained data infrastructure for real-time AIS ingestion, vessel tracking, and anomaly detection, and re-architected a ClickHouse-based ETL pipeline to cut job latency by 90-95%.

Python Dagster Docker Polars Pandas Pyarrow Clickhouse Grafana Superset DuckDB Parquet Slack ETL Data Pipelines Real Time Analytics

AI Automation Engineer (Internship)

Agentiwise AI Agency

Jun 2025 - Nov 2025 (5 months)

Designed and developed 70+ LLM-based applications using single-agent and multi-agent patterns for business use cases, including RAG agents, an AI travel agent, and OCR-based invoice extraction. Built backend APIs using Python (FastAPI) and documented reusable components.

LLM RAG Graph RAG N8n OpenAI Agent Builder fastAPI Python API Development Multi Agent Systems OCR GPT

Data Analyst / Engineer

Reveal HealthTech

Jun 2024 - Jun 2025 (1 year)

Designed a fully automated end-to-end ELT data pipeline using Airbyte, DBT-Redshift, Dagster, and Superset, achieving 100% automation and improving reporting speed by over 40%. Implemented QA checks maintaining a 99% error-free rate across DBT models.