AI Engineer with 3+ years in Agentic AI, Multi-Agent Systems & LLM Platforms
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AI Engineer with 3+ years of experience building production-grade Agentic AI, Multi-Agent Systems, LLM applications, RAG solutions, and Machine Learning platforms. Proven track record of delivering scalable AI systems that automate business processes and drive measurable business impact.
Pune University
B.Tech · Computer Engineering
August 1, 2019 – June 1, 2023
Hoonartek
Associate Consultant
July 1, 2023 – Present
Pune, Maharashtra, India
AI-Powered Logistics Intelligence Platform
April 1, 2026 – Present
Developed an AI-powered analytics platform enabling business users to analyze shipment profitability, lane performance, customer trends, and root-cause insights through natural language queries, eliminating dependency on manual reporting. Architected a scalable BigQuery-based analytics foundation and semantic data model to support governed, enterprise-wide decision intelligence. Built a secure NL-to-SQL Data Agent with guardrails and role-based access controls, enabling self-service analytics while maintaining data governance and compliance. Implemented feedback-driven learning, recursive self-improvement, and observability frameworks to continuously enhance agent accuracy, reliability, and decision quality.
Enterprise HR Attrition Intelligence Platform
January 1, 2026 – Present
Built an AI-powered workforce intelligence platform to predict employee attrition and enable proactive retention strategies. Developed explainable ML models and workforce risk scoring pipelines for enterprise-wide attrition prediction and scenario analysis. Delivered real-time and batch prediction capabilities to support data-driven workforce planning and decision-making. Architected scalable Lakehouse, Feature Store, and MLOps frameworks on Databricks, enabling reliable model deployment, governance, and monitoring. Implemented enterprise-grade MLOps using Databricks Model Registry, Model Serving, and drift detection frameworks, enabling governed model lifecycle management, continuous monitoring, and scalable production deployment.
Sales Lead Intelligence & Lookup System
September 1, 2025 – February 1, 2026
Built an AI-powered sales intelligence platform that automated lead discovery, company research, competitive intelligence, and personalized outreach through natural language interactions. Integrated Krips MCP to pull real-time meeting notes and context, enabling the system to generate meeting summaries and align outreach with live conversation intelligence. Enabled real-time sales insights by integrating meeting intelligence, market signals, and enterprise data sources into a unified decision-support workflow. Reduced lead research and outreach preparation time by 80%, allowing sales teams to focus on high-value customer engagement and pipeline growth.
Cultural Fit Analysis
The candidate's project portfolio showcases a diverse range of applications for AI/ML across different industries (logistics, HR, sales, general enterprise support), indicating adaptability and a broad interest in applying AI to various business problems. Their experience with multiple AI paradigms (Agentic AI, LLMs, RAG, traditional ML) and platforms (LangChain, Databricks, Google ADK) suggests a willingness to learn and integrate new technologies. The focus on delivering measurable business impact aligns well with a results-oriented culture. The candidate's current role as an Associate Consultant further supports their ability to work on varied projects and adapt to different client needs.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project descriptions, tackling complex business challenges with AI-driven solutions. Their experience in leading development and architecting systems suggests good initiative and ownership. The focus on enterprise-scale solutions implies an understanding of operational requirements and scalability. However, direct evidence of teamwork, stress handling, or communication in a collaborative setting is not explicitly detailed in the provided data.