Atharva Rahate

About Atharva Rahate

I work in the intersection of data engineering and applied AI, building systems that are built to run in the real world, thus in production and not just on the notebook.My main focus in data is designing, maintaining data pipelines and preprocessing workflow and ML ready pipelines that supports analytics, machine learning and production deployment. I care passionately about data quality, system reliability and ensuring data models and applications downstream have clean well structured inputs they can actually rely on.Alongside this, I have significant hands-on experience with applied AI and machine learning, such as computer vision, transformers and LLM optimization. I\'ve worked on end-to-end systems where you had to have data ingestion working with feature engineering working with training a model working with deployment working with monitoring seamlessly. This has formed the way I think: models are only as good as the data systems that they\'re based on.One of my significant projects is Clarivo; where I dedicated time to build structured, scalable foundation of data driven intelligence connecting pipeline of data, processing layers, AI components as cohesive system designed to be used in real life. The focus all along has been on clarity, robustness and operational rather than experimentation complexity.I’ve worked across internships, personal projects, and engineering teams, contributing to production ML systems, improving data preprocessing pipelines, optimizing inference workflows, and deploying services using modern tooling. I’m comfortable taking end-to-end ownership of problems from raw data ingestion to deployed services and I value clean interfaces between data systems, models, and backend services.Technically, I do mostly work in Python, SQL, ETL workflows, TensorFlow, PyTorch, FastAPI, Docker, CI/CD and I\'m constantly increasing the level of exposure to cloud-based data infrastructure. My long term direction is toward building big data size platfroms that are reliable and enable analytics and intelligent systems in production.

Experience

1. Data Engineer

   Celebal Technologies

   Jan 2026 — Present · Pune, IN

   Working on the development of Aquila / EagleEye, an Agentic AI–based data observability platform designed to automate data quality monitoring, root cause analysis, and operational analytics across enterprise data pipelines. Contributed to building a multi-agent architecture including Intent Router, Orchestrator, KPI, RCA, Table Health, and Data Quality agents, enabling natural language queries to be automatically interpreted and routed to the appropriate system workflows. Implemented LLM-assisted pipelines for generating data quality scenarios, analyzing failures, and producing RCA insights by leveraging lineage metadata, transformation logic, and profiling outputs. Integrated Databricks and Unity Catalog to support automated data generation, transformation, and validation workflows, and exposed the orchestration layer through Flask APIs for system integration. Focused on improving system reliability and performance through stress testing, prompt engineering, routing logic improvements, and parallel workflow execution, significantly optimizing pipeline performance and enabling scalable enterprise-grade AI-driven data operations.

Education

• PUNE VIDYARTHI GRIHA'S COLLEGE OF ENGINEERING, NASHIK

  Bachelor of Engineering - BE, Computer Engineering

  2022 — 2026