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I’m an AI/ML Engineer with a passion for building scalable, safe, and impactful multimodal AI systems. Over the past 5+ years, I’ve worked on production ML solutions at OpenAI and Adobe, focusing on generative models, real-time personalization, and cross-modal retrieval. I enjoy turning research breakthroughs into reliable features that teams can ship and measure in the wild. I thrive in cross-functional settings, collaborating with Research, Product, and Safety to craft robust data pipelines, evaluation frameworks, and monitoring dashboards. IRO and safety-first thinking guide my work, ensuring models perform well while minimizing policy violations and unintended outcomes. I’m proficient in Python, PyTorch, Spark, Kafka, and distributed training on A100/H100 clusters, and I love building end-to-end systems that are scalable, maintainable, and auditable.…I’m an AI/ML Engineer with a passion for building scalable, safe, and impactful multimodal AI systems. Over the past 5+ years, I’ve worked on production ML solutions at OpenAI and Adobe, focusing on generative models, real-time personalization, and cross-modal retrieval. I enjoy turning research breakthroughs into reliable features that teams can ship and measure in the wild. I thrive in cross-functional settings, collaborating with Research, Product, and Safety to craft robust data pipelines, evaluation frameworks, and monitoring dashboards. IRO and safety-first thinking guide my work, ensuring models perform well while minimizing policy violations and unintended outcomes. I’m proficient in Python, PyTorch, Spark, Kafka, and distributed training on A100/H100 clusters, and I love building end-to-end systems that are scalable, maintainable, and auditable.

Nikesh Bodduluri

AI Developer, AI Engineer, Data Scientist, +2

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I’m an AI/ML Engineer with a passion for building scalable, safe, and impactful multimodal AI systems. Over the past 5+ years, I’ve worked on production ML solutions at OpenAI and Adobe, focusing on generative models, real-time personalization, and cross-modal retrieval. I enjoy turning research breakthroughs into reliable features that teams can ship and measure in the wild. I thrive in cross-functional settings, collaborating with Research, Product, and Safety to craft robust data pipelines, evaluation frameworks, and monitoring dashboards. IRO and safety-first thinking guide my work, ensuring models perform well while minimizing policy violations and unintended outcomes. I’m proficient in Python, PyTorch, Spark, Kafka, and distributed training on A100/H100 clusters, and I love building end-to-end systems that are scalable, maintainable, and auditable.…I’m an AI/ML Engineer with a passion for building scalable, safe, and impactful multimodal AI systems. Over the past 5+ years, I’ve worked on production ML solutions at OpenAI and Adobe, focusing on generative models, real-time personalization, and cross-modal retrieval. I enjoy turning research breakthroughs into reliable features that teams can ship and measure in the wild. I thrive in cross-functional settings, collaborating with Research, Product, and Safety to craft robust data pipelines, evaluation frameworks, and monitoring dashboards. IRO and safety-first thinking guide my work, ensuring models perform well while minimizing policy violations and unintended outcomes. I’m proficient in Python, PyTorch, Spark, Kafka, and distributed training on A100/H100 clusters, and I love building end-to-end systems that are scalable, maintainable, and auditable.

Available to hire

I’m an AI/ML Engineer with a passion for building scalable, safe, and impactful multimodal AI systems. Over the past 5+ years, I’ve worked on production ML solutions at OpenAI and Adobe, focusing on generative models, real-time personalization, and cross-modal retrieval. I enjoy turning research breakthroughs into reliable features that teams can ship and measure in the wild.

I thrive in cross-functional settings, collaborating with Research, Product, and Safety to craft robust data pipelines, evaluation frameworks, and monitoring dashboards. IRO and safety-first thinking guide my work, ensuring models perform well while minimizing policy violations and unintended outcomes. I’m proficient in Python, PyTorch, Spark, Kafka, and distributed training on A100/H100 clusters, and I love building end-to-end systems that are scalable, maintainable, and auditable.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at OpenAI

January 1, 2024 - Present

Contributed to multimodal model training pipelines for GPT-4V and Sora, processing 500B+ token-equivalent image-text and video-text pairs using distributed data loading across OpenAI's A100/H100 GPU clusters. Built automated video-text alignment pipelines for data curation, implemented scene segmentation, caption quality filtering, and temporal consistency scoring that improved training data quality metrics by 29%. Developed vision-language evaluation frameworks with perceptual metrics (FID, CLIPScore, VQAv2 accuracy) and human preference protocols, reducing evaluation turnaround by 38%. Designed multimodal embedding alignment pipelines using contrastive learning, improving cross-modal retrieval by 26%. Contributed to Sora's video generation model inference optimization with temporal attention caching and mixed-precision serving, reducing per-video generation latency by 32% at production scale. Partnered with Safety and Alignment teams to integrate multimodal content classifiers, reduc

Machine Learning Engineer at Adobe

March 1, 2018 - June 1, 2022

Built productionized customer propensity scoring models using gradient boosted trees (XGBoost/LightGBM) and deep learning architectures to predict purchase intent, churn risk, and upsell likelihood across 100M+ profiles. Developed real-time audience segmentation pipelines with Apache Spark and Adobe Experience Platform (AEP), refreshing segments sub-hourly. Built content recommendation models using collaborative filtering and neural embeddings, improving content click-through rates by ~27% in controlled AB tests on Adobe Target. Designed multi-touch attribution frameworks identifying high-value conversion pathways, enabling a 20% uplift in attributed ROI. Implemented real-time personalization scoring with Apache Kafka and Redis, achieving sub-25ms p95 inference latency in a multi-tenant architecture. Migrated batch feature pipelines to streaming on Azure Event Hubs, reducing feature staleness from 24 hours to 45 minutes. Built automated model monitoring and data drift detection across

ML Engineer at Adobe (Experience Cloud) - India

March 1, 2018 - June 1, 2022

Built productionized propensity scoring models using gradient-boosted trees (XGBoost/LightGBM) and deep learning for 100M+ profiles (purchase intent, churn, upsell). Developed real-time audience segmentation pipelines with Apache Spark and Adobe Experience Platform, delivering sub-hourly segment refreshes. Created content recommendation models with collaborative filtering and neural embeddings, achieving ~27% uplift in content CTR in Adobe Target AB tests. Designed multi-touch attribution models to optimize marketing spend, delivering ~20% ROI uplift. Implemented real-time personalization scoring with Kafka and Redis, achieving sub-25ms p95 latency in a multi-tenant Sensei environment. Migrated batch features to streaming on Azure Event Hubs, reducing feature staleness from 24h to 45m. Built automated model monitoring and drift detection across 20+ features, cutting time to detect degradation by ~43%. Standardized offline (AUC-ROC, PR) and online (conversion lift, revenue per visitor)