Open to opportunities

Kunal Bhardwaj

@kunalbhardwaj

Message

Data Scientist building agentic AI and RAG systems that improve real-world outcomes.

India

Message

What I'm looking for

I’m looking to build production AI—agentic systems, RAG, and MLOps—where I can ship end-to-end solutions, collaborate with product and engineering, and drive measurable improvements through experimentation and responsible model deployment.

I’m a Data Scientist and Applied AI Scientist focused on building end-to-end ML systems with transformer models, RAG architectures, and agentic workflows. At HRS GROUP, I architected solutions that delivered a 30% improvement in customer query resolution accuracy, built production data pipelines processing 10M+ daily events with AWS Glue and Apache Airflow, and drove MLOps with CI/CD for model versioning, monitoring, and automated retraining.

I also lead with product impact: I designed predictive dashboards for C-suite decision-making and built an agentic AI platform that turns problem statements into structured project workflows, including Jira-ready task breakdowns. Earlier, as a Technical Product Manager Intern, I developed ML-driven features with projected 2% revenue impact, and I led an AI research team working on computer vision waste classification—grounded in practical deployment and measurable outcomes.

Experience

Work history, roles, and key accomplishments

Data Scientist

HRS Group

Oct 2023 - May 2026 (2 years 7 months)

Designed and shipped an agentic AI platform that converts natural-language requests into structured workflows and Jira tickets, achieving 60% autonomous task completion through multi-agent planning and tool orchestration.
• Architected a production RAG pipeline (LangChain, embeddings, vector retrieval) over enterprise knowledge bases, improving query-resolution accuracy by 30%.

Airflow Transformer Models RAG Agentic AI AWS Glue Data Pipelines MLOps CI CD Model Versioning Model Monitoring

Technical Product Manager Intern

Apptio

Apr 2023 - Jun 2023 (2 months)

Specified ML-driven features (customer segmentation, anomaly detection) with projected 2% revenue impact, authoring PRDs and translating technical design into clear stakeholder narratives.

Customer Segmentation Recommendation Systems Anomaly Detection Technical Specifications User Stories

AI Research Team Lead & Incubatee

Atal Incubation Centre

Sep 2021 - Apr 2022 (7 months)

Led a cross-functional team developing a computer vision-based waste classification system using CNN architectures and transfer learning. Served as an AI research team lead and incubatee for the project.

Computer Vision CNN Architectures Transfer Learning Team Leadership