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AI/ML Engineer with 3+ years of experience building production-grade machine learning and Generative AI systems in enterprise environments. I specialize in Retrieval-Augmented Generation (RAG), intelligent document processing, predictive modeling, and AI-driven automation using Python, SQL, LangChain, OpenAI API, FastAPI, and AWS. I focus on feature engineering, prompt engineering, model evaluation, vector search, and MLOps—delivering scalable AI systems that improve operational efficiency, support analytics, and enable data-driven decision making. I’ve deployed LLM and ML services with FastAPI/Docker, implemented monitoring and drift detection, and built evaluation frameworks to ensure reliability in production.…AI/ML Engineer with 3+ years of experience building production-grade machine learning and Generative AI systems in enterprise environments. I specialize in Retrieval-Augmented Generation (RAG), intelligent document processing, predictive modeling, and AI-driven automation using Python, SQL, LangChain, OpenAI API, FastAPI, and AWS. I focus on feature engineering, prompt engineering, model evaluation, vector search, and MLOps—delivering scalable AI systems that improve operational efficiency, support analytics, and enable data-driven decision making. I’ve deployed LLM and ML services with FastAPI/Docker, implemented monitoring and drift detection, and built evaluation frameworks to ensure reliability in production.

Abhishek Reddy

AI Engineer, Data Scientist, Programmer, +3

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AI/ML Engineer with 3+ years of experience building production-grade machine learning and Generative AI systems in enterprise environments. I specialize in Retrieval-Augmented Generation (RAG), intelligent document processing, predictive modeling, and AI-driven automation using Python, SQL, LangChain, OpenAI API, FastAPI, and AWS. I focus on feature engineering, prompt engineering, model evaluation, vector search, and MLOps—delivering scalable AI systems that improve operational efficiency, support analytics, and enable data-driven decision making. I’ve deployed LLM and ML services with FastAPI/Docker, implemented monitoring and drift detection, and built evaluation frameworks to ensure reliability in production.…AI/ML Engineer with 3+ years of experience building production-grade machine learning and Generative AI systems in enterprise environments. I specialize in Retrieval-Augmented Generation (RAG), intelligent document processing, predictive modeling, and AI-driven automation using Python, SQL, LangChain, OpenAI API, FastAPI, and AWS. I focus on feature engineering, prompt engineering, model evaluation, vector search, and MLOps—delivering scalable AI systems that improve operational efficiency, support analytics, and enable data-driven decision making. I’ve deployed LLM and ML services with FastAPI/Docker, implemented monitoring and drift detection, and built evaluation frameworks to ensure reliability in production.

Available to hire

AI/ML Engineer with 3+ years of experience building production-grade machine learning and Generative AI systems in enterprise environments. I specialize in Retrieval-Augmented Generation (RAG), intelligent document processing, predictive modeling, and AI-driven automation using Python, SQL, LangChain, OpenAI API, FastAPI, and AWS.

I focus on feature engineering, prompt engineering, model evaluation, vector search, and MLOps—delivering scalable AI systems that improve operational efficiency, support analytics, and enable data-driven decision making. I’ve deployed LLM and ML services with FastAPI/Docker, implemented monitoring and drift detection, and built evaluation frameworks to ensure reliability in production.

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Work Experience

AI/ML Engineer at Northern Trust

January 1, 2025 - Present

Developed RAG applications using LangChain, OpenAI API, and Pinecone enabling semantic search across 750K+ financial documents and reducing analyst research time by 58%. Built LLM-based intelligent document processing workflows with validation pipelines to extract structured compliance information, improving accuracy by 29% and reducing manual review effort. Designed prompt engineering and evaluation frameworks for enterprise AI assistants, improving response relevance by 24% through retrieval tuning and structured metrics. Built customer risk prediction models on 10M+ customer records using Python, SQL, and XGBoost (precision +19%). Automated feature engineering and training using MLflow and Vertex AI Pipelines to reduce deployment time by 41% and improve reproducibility. Deployed AI/ML services via FastAPI and Docker for 30K+ monthly inference requests, and implemented monitoring/drift detection using SageMaker Model Monitor and CloudWatch to reduce degradation incidents by 38%.

Machine Learning Engineer at Accenture

August 1, 2021 - July 1, 2023

Built ML pipelines with Python, SQL, Scikit-learn, and PySpark to process 2M+ enterprise records, reducing feature preparation time by 35%. Developed customer segmentation and classification models using Random Forest and XGBoost (precision +18%). Designed reusable feature engineering pipelines to standardize preprocessing across ML models, decreasing development effort by 40% and improving training consistency. Created NLP workflows for document classification and entity extraction using transformer-based models, reducing manual processing effort by 32%. Improved model generalization by 21% via cross-validation, hyperparameter optimization, and statistical testing. Created Power BI dashboards for accuracy, feature importance, and KPIs to help stakeholders monitor model performance and operational outcomes.