Tarun Khurana
@tarunkhurana
Senior AI/ML Engineer delivering end-to-end, production-ready ML and GenAI systems.
What I'm looking for
I’m a Senior AI/ML Engineer with 5+ years architecting, training, and deploying machine learning models and AI-powered systems in production. I bring deep expertise across the full ML lifecycle—from data engineering and feature development to model training, deployment, and monitoring—plus hands-on LLM integration, RAG pipelines, and GenAI workflows on AWS.
I consistently drive measurable business outcomes: ~15% lift in customer retention, 50% faster knowledge retrieval, and $200K+ in cost savings. At TCS, I built AWS Bedrock–based internal knowledge retrieval APIs, automated feature engineering to improve training turnaround by 2x, and implemented drift detection with retraining triggers to reduce degradation incidents by 30%—while mentoring junior engineers and standardizing production-ready practices.
Experience
Work history, roles, and key accomplishments
Architected end-to-end ML pipelines for customer churn prediction, improving client retention by ~15% across 3 major accounts. Built AWS Bedrock LLM integration for knowledge retrieval, reducing analyst research time by 50%, and implemented automated feature engineering and MLOps monitoring to cut manual prep 40% and reduce degradation incidents by 30%.
Developed predictive models on 10M+ record datasets to support retention and cross-sell strategy recommendations. Automated reporting to cut generation time by 60%, optimized SQL Server/Oracle workloads for up to 35% faster execution, and created Power BI dashboards replacing 15+ legacy Excel reports.
Education
Degrees, certifications, and relevant coursework
NMIMS (Narsee Monjee Institute of Management Studies)
Master of Business Administration, Business Management
2021 - 2023
Earned an MBA in Business Management from NMIMS from 2021 to 2023.
UPES (University of Petroleum and Energy Studies)
Bachelor of Technology, Computer Science
2016 - 2020
Earned a B.Tech in Computer Science from UPES from 2016 to 2020.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Salary expectations
Job categories
Skills
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