Generative AI Engineer with 2+ years in ML Engineering & Data Science
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Highly skilled professional with 2.4 years of experience specializing in ML Engineering, Generative AI, and Data Science. Proven ability to design and deploy AI-driven solutions, including multi-agent systems, LLM pipelines, and predictive models. Proficient in a wide array of tools and technologies such as LangChain, OpenAI, Databricks, Google Cloud, Python, and SQL, with a strong focus on delivering actionable business recommendations and automating complex workflows.
Christ(Deemed to be)University
Master of Science · Data Science
August 1, 2022 – June 30, 2024
Loyalytics AI
Associate Data Analyst
July 1, 2025 – Present
India
Hudson's Bay Company
Data Scientist
June 1, 2024 – May 1, 2025
India
Aiqwip
Gen AI Engineer
January 1, 2024 – May 1, 2024
India
ChurnShield: Multi-Agent Retail Churn System
June 25, 2026 – Present
Architected LangGraph-based multi-agent pipeline combining BG/NBD probabilistic scoring with Gemini reasoning and enabling personalized retention strategies for 4,189+ customers with £62K+ CLV at risk, translating probabilistic churn scores into actionable business recommendations Deployed FastAPI + Streamlit service layer with 9 REST endpoints enabling real-time customer risk queries, conditional alert routing across 4 SLA tiers, Excel reporting and deployed on Cloud Run. Implemented stateful StateGraph architecture with 5 agents (data cleaning, probabilistic scoring, insight generation, retention offers, alert) achieving 64.9% churn detection accuracy.
GenHire
June 25, 2026 – Present
Reduced candidate screening cycle from 7 days to 48 hours by designing and deploying an end-to-end GenAI resume parsing and JD-matching pipeline containerised with Docker on Google Cloud Run. Eliminated manual resume review effort by engineering a Gemini 2.5 Flash + LangChain pipeline with Chain-of-Thought prompting for structured extraction and weighted JD scoring. Ensured near 98% parse coverage for image-based and scanned resumes by implementing an OCR fallback mechanism, reducing data loss from non-standard PDF formats. Automated post-shortlisting candidate outreach by integrating Google Sheets output with AppScript-triggered interest confirmation emails, reducing manual HR coordination effort entirely.
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for a dynamic, innovation-driven environment, particularly in AI/ML. The diversity of projects (churn prediction, HR screening, loan processing, meeting summarization) and the application of advanced AI techniques indicate adaptability and a proactive approach to leveraging new technologies. The experience across different company types (Loyalytics AI, Hudson's Bay Company, Aiqwip) also suggests an ability to integrate into various organizational cultures.
Soft Skills & Operational Fit
The candidate's project descriptions and experience highlight strong problem-solving skills, an ability to translate business problems into technical solutions, and a focus on efficiency and automation. The experience in leading development of an MVP and architecting stateful systems suggests good operational fit for a senior role requiring end-to-end ownership.