Hello! I’m Divya Atluri, an AI/ML Engineer with 3+ years of experience designing and deploying scalable machine learning systems, LLM-powered applications, and data-driven solutions across enterprise environments. I specialize in Generative AI, MLOps, and cloud-native AI systems on AWS, Azure, and GCP, with a track record of improving model performance, reducing inference latency, and delivering production-grade AI systems serving millions of requests. I have hands-on experience across the full ML lifecycle—data engineering, model development, deployment, monitoring, and optimization—working with teams to build robust pipelines, governance, and observability for reliable, secure AI at scale. My recent work includes enterprise GenAI knowledge assistants, RAG pipelines, and scalable vector search architectures, complemented by strong collaboration with cross-functional stakeholders.

Divya Atluri

Hello! I’m Divya Atluri, an AI/ML Engineer with 3+ years of experience designing and deploying scalable machine learning systems, LLM-powered applications, and data-driven solutions across enterprise environments. I specialize in Generative AI, MLOps, and cloud-native AI systems on AWS, Azure, and GCP, with a track record of improving model performance, reducing inference latency, and delivering production-grade AI systems serving millions of requests. I have hands-on experience across the full ML lifecycle—data engineering, model development, deployment, monitoring, and optimization—working with teams to build robust pipelines, governance, and observability for reliable, secure AI at scale. My recent work includes enterprise GenAI knowledge assistants, RAG pipelines, and scalable vector search architectures, complemented by strong collaboration with cross-functional stakeholders.

Available to hire

Hello! I’m Divya Atluri, an AI/ML Engineer with 3+ years of experience designing and deploying scalable machine learning systems, LLM-powered applications, and data-driven solutions across enterprise environments. I specialize in Generative AI, MLOps, and cloud-native AI systems on AWS, Azure, and GCP, with a track record of improving model performance, reducing inference latency, and delivering production-grade AI systems serving millions of requests.

I have hands-on experience across the full ML lifecycle—data engineering, model development, deployment, monitoring, and optimization—working with teams to build robust pipelines, governance, and observability for reliable, secure AI at scale. My recent work includes enterprise GenAI knowledge assistants, RAG pipelines, and scalable vector search architectures, complemented by strong collaboration with cross-functional stakeholders.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert

Language

English
Fluent

Work Experience

AI/ML Engineer at OpenAI
January 1, 2025 - Present
Built an enterprise GenAI knowledge assistant with Retrieval-Augmented Generation (RAG) system, designed large-scale LLM-based pipelines using OpenAI APIs and LangChain, and implemented scalable vector search architecture (Pinecone) handling 5M+ documents. Reduced hallucination rate by ~42% using hybrid semantic + keyword retrieval and optimized inference with vLLM and batching to cut latency from 2.8s to 1.1s. Developed a multi-agent workflow system for automated tasks across enterprise APIs, and established an MLOps stack (MLflow + Kubernetes + AWS SageMaker) enabling 99.9% uptime. Implemented secure authentication and SOC2-aligned data governance, and deployed monitoring dashboards (Prometheus + Grafana) for model drift and token usage. Processed 1M+ daily queries, improving productivity by ~30–45% and reducing AI costs by ~25%.
Data Scientist at Wipro
June 1, 2021 - July 1, 2023
Built predictive models and analytics dashboards to improve customer retention, sales forecasting, and operational decision-making for enterprise banking and retail clients. Developed customer churn prediction model using XGBoost achieving 87% accuracy; ML-driven segmentation improved retention by 22%; designed demand forecasting models (ARIMA, Prophet) reducing forecast error by 18%. Designed ETL pipelines and interactive dashboards (Power BI); automated data preprocessing reducing manual effort by 60%. Feature engineering across 10M+ transactional records; deployed ML models using Azure ML Studio with REST APIs.

Education

Master of Science in Data Analytics Engineering at George Mason University, Virginia, USA
August 1, 2023 - May 1, 2025

Qualifications

Add your qualifications or awards here.

Industry Experience

Software & Internet, Professional Services