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I'm an AI/ML Engineer with 3+ years of experience building and deploying machine learning and retrieval-based systems in production. I focus on turning data into reliable, scalable decisions, with applications ranging from customer risk modeling to document intelligence. I design end-to-end ML pipelines, explore RAG architectures, and run rigorous experimentation to boost accuracy and speed in enterprise contexts. I enjoy cross-functional collaboration and delivering impact through robust systems and thoughtful prompts.…I'm an AI/ML Engineer with 3+ years of experience building and deploying machine learning and retrieval-based systems in production. I focus on turning data into reliable, scalable decisions, with applications ranging from customer risk modeling to document intelligence. I design end-to-end ML pipelines, explore RAG architectures, and run rigorous experimentation to boost accuracy and speed in enterprise contexts. I enjoy cross-functional collaboration and delivering impact through robust systems and thoughtful prompts.

Abhishek Reddy

AI Engineer, Data Scientist, AI Developer, +3

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I'm an AI/ML Engineer with 3+ years of experience building and deploying machine learning and retrieval-based systems in production. I focus on turning data into reliable, scalable decisions, with applications ranging from customer risk modeling to document intelligence. I design end-to-end ML pipelines, explore RAG architectures, and run rigorous experimentation to boost accuracy and speed in enterprise contexts. I enjoy cross-functional collaboration and delivering impact through robust systems and thoughtful prompts.…I'm an AI/ML Engineer with 3+ years of experience building and deploying machine learning and retrieval-based systems in production. I focus on turning data into reliable, scalable decisions, with applications ranging from customer risk modeling to document intelligence. I design end-to-end ML pipelines, explore RAG architectures, and run rigorous experimentation to boost accuracy and speed in enterprise contexts. I enjoy cross-functional collaboration and delivering impact through robust systems and thoughtful prompts.

Available to hire

I’m an AI/ML Engineer with 3+ years of experience building and deploying machine learning and retrieval-based systems in production. I focus on turning data into reliable, scalable decisions, with applications ranging from customer risk modeling to document intelligence.

I design end-to-end ML pipelines, explore RAG architectures, and run rigorous experimentation to boost accuracy and speed in enterprise contexts. I enjoy cross-functional collaboration and delivering impact through robust systems and thoughtful prompts.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at JPMorgan Chase & Co.

January 1, 2025 - Present

Developed and deployed ML models for customer churn prediction and lead scoring on 10M+ customer records; integrated outputs into CRM workflows, improving outreach prioritization precision by 18%. Implemented RAG pipelines using vector search and contextual reranking to retrieve relevant financial documents for LLM responses; reduced factual inconsistencies in compliance-related queries by 32% and decreased analyst lookup time to under 2 minutes. Designed prompt chaining workflows for GPT-based applications, introduced validation checks and conducted A/B tests; improved task completion rates by 22% and reduced follow-up clarification requests by 35%. Built experimentation pipelines using statistical testing (t-tests, bootstrap confidence intervals) to evaluate model and prompt performance, enabling faster decision-making and reducing validation cycle time from 3 weeks to 8 days across use cases. Deployed and managed ML pipelines on GCP Vertex AI, supporting batch inference and model ve

Data Scientist at Coforge

August 1, 2021 - July 31, 2023

Engineered data pipelines and feature engineering workflows to support ML models for demand forecasting, processing 2M+ supply chain records per batch across 6 business units; improved data quality and reduced preprocessing time by 60%, enabling more reliable model training. Designed and maintained a centralized feature repository in SQL Server, standardized feature definitions across 4+ predictive models, reducing redundant data transformations by 45%. Developed demand forecasting models using LSTM and XGBoost ensembles across 15+ distribution nodes; improved 30-day demand prediction accuracy by 17% (MAPE) and reduced overstock incidents by 12%. Evaluated forecasting models using cross-validation, backtesting, and error analysis, improving generalization. Delivered Power BI dashboards to monitor model performance, forecast accuracy, and anomaly patterns, enabling 20+ stakeholders to track ML-driven decisions and reducing ad-hoc analytical requests by 50%. Partnered with operations and