Sharad Jain

Founder of autoscreen.ai, building agentic AI for enterprise.

Bengaluru, India

LinkedInX
SJ

Education

University of California Davis, Graduate School of Management

2017 - 2018
MS in Business Analytics

University Institute of Technology, Bhopal

2011 - 2015
BS in Mechanical Engineering

About

Founder of autoscreen.ai with a mission to build the next generation of agentic AI for enterprise. My work involves designing and implementing advanced AI systems that can reason, plan, and execute complex tasks with minimal human intervention. I have extensive experience in building scalable data pipelines, developing MLOps best practices, and leveraging state-of-the-art frameworks like chain of thought, graph of thoughts, and multi-agent collaboration to create intelligent and autonomous solutions.

Work Experience

autoscreen.ai
Bengaluru

2023 - Present

Founder

  • Set the strategic vision and technical roadmap, leading a team of engineers and researchers to develop cutting-edge agentic AI solutions.
  • Drove the end-to-end product lifecycle, from initial concept and fundraising to successful deployment and widespread customer adoption.
  • Fostered a culture of innovation and rapid iteration, establishing MLOps best practices and agile development methodologies to accelerate product delivery.
  • Architected a scalable, production-grade conversational AI for real-time voice, achieving <500ms latency. The agentic, tool-based system is deployed on Kubernetes, supporting over 100 concurrent sessions.
  • Engineered a sophisticated RAG system to ground a text-to-SQL agent in complex schemas, leveraging a custom vector store and evaluation framework (Recall@k, MRR) to minimize hallucinations and boost accuracy.
  • Designed a robust communication backbone for multi-agent workflows, using OpenAI function-calling to enforce strict JSON schemas for seamless task decomposition and reliable interaction between 3+ specialized agents.
  • Built an automated multimodal content pipeline using visual AI (GPT-4.1-mini) to analyze video, generate contextual summaries, and synthesize narration, reducing manual content creation by over 80%.

withjoy.com
San Francisco, CA

2020 - 2023

Sr. Data Engineer

  • Spearheaded monthly cross-platform marketing analytics, improving channel performance by 20% YoY through data-driven optimizations.
  • Authored and automated Python DAGs in Airflow, reducing data team workload by over 60% and establishing data-pipelines-as-code.
  • Developed efficient ingestion, ETL, and reverse ETL pipelines, improving data processing efficiency by 40%.

Meta (Facebook)
Menlo Park, CA

2018 - 2020

Data Scientist

  • Implemented MLOps best practices, accelerating model deployment by 40% and cutting production issues by 30%.
  • Engineered scalable data pipelines using Apache Spark and Airflow to process over 10TB of data daily, boosting data freshness by 50% and model accuracy by 15%.
  • Optimized user onboarding through A/B testing, reducing activation time from 7 to 2 days and increasing new user retention by 30%.

Autodesk
San Francisco, CA

2017 - 2018

Data Scientist

  • Implemented ML models to predict transient infrastructure failures, improving prediction accuracy by 15% through advanced feature engineering.
  • Built and maintained an AWS Quicksight dashboard for business insights, supporting the Data Platform Team in predictive maintenance efforts.

Tata Consultancy Services
Pune, India

2015 - 2017

Data Analyst

  • Designed and implemented complex ETL mappings using IBM Infosphere DataStage for large-scale data warehousing projects.
  • Performed detailed impact analysis on existing ETL mappings to support evolving business requirements and optimize performance.

Skills

Agentic AI
LLM
RAG
Python
SQL
ETL
AWS
Airflow
Kubernetes
MLOps
A/B Testing
Natural Language Processing
Data Engineering

Open Source Activity

GitHub Contributions

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Projects

Agentic Conversational AI

Architected a real-time voice AI with <500ms latency, supporting 100+ concurrent sessions.

LiveKit
Twilio
Gemini API
Kubernetes

Advanced RAG System

Engineered a RAG system to ground a text-to-SQL agent, improving accuracy and reducing hallucinations.

Vector Stores
Recall@k
MRR

Multi-Agent Workflow Backbone

Designed a structured communication system for 3+ AI agents, ensuring reliable task decomposition.

OpenAI Function Calling
JSON Schema

Automated Multimodal Content Pipeline

Developed a pipeline that uses visual AI to analyze video and generate summaries, reducing content creation efforts by 80%.

GPT-4.1-mini
TTS