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AI/ML Engineer with 4+ years of experience building scalable machine learning systems, LLM-powered applications, and distributed AI infrastructure across recommendation and enterprise platforms. Skilled in PyTorch, Spark, Kubernetes, Generative AI, and MLOps, with proven success improving model accuracy, inference performance, and workflow automation. Passionate about developing production-grade AI solutions, agentic systems, and high-performance ML platforms that drive measurable business impact.…AI/ML Engineer with 4+ years of experience building scalable machine learning systems, LLM-powered applications, and distributed AI infrastructure across recommendation and enterprise platforms. Skilled in PyTorch, Spark, Kubernetes, Generative AI, and MLOps, with proven success improving model accuracy, inference performance, and workflow automation. Passionate about developing production-grade AI solutions, agentic systems, and high-performance ML platforms that drive measurable business impact.

Aryan Thottempudi

AI Engineer, Software Engineer, DevOps Developer, +2

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AI/ML Engineer with 4+ years of experience building scalable machine learning systems, LLM-powered applications, and distributed AI infrastructure across recommendation and enterprise platforms. Skilled in PyTorch, Spark, Kubernetes, Generative AI, and MLOps, with proven success improving model accuracy, inference performance, and workflow automation. Passionate about developing production-grade AI solutions, agentic systems, and high-performance ML platforms that drive measurable business impact.…AI/ML Engineer with 4+ years of experience building scalable machine learning systems, LLM-powered applications, and distributed AI infrastructure across recommendation and enterprise platforms. Skilled in PyTorch, Spark, Kubernetes, Generative AI, and MLOps, with proven success improving model accuracy, inference performance, and workflow automation. Passionate about developing production-grade AI solutions, agentic systems, and high-performance ML platforms that drive measurable business impact.

Available to hire

AI/ML Engineer with 4+ years of experience building scalable machine learning systems, LLM-powered applications, and distributed AI infrastructure across recommendation and enterprise platforms. Skilled in PyTorch, Spark, Kubernetes, Generative AI, and MLOps, with proven success improving model accuracy, inference performance, and workflow automation.

Passionate about developing production-grade AI solutions, agentic systems, and high-performance ML platforms that drive measurable business impact.

Skills

Work Experience

AI/ML Engineer at Meta

February 1, 2025 - Present

Developed Python-based autonomous ML experimentation workflows using PyTorch and TorchRec to enable distributed ranking model validation and accelerate iteration cycles for large-scale recommendation systems. Engineered LLM-driven hypothesis generation/evaluation pipelines improving ranking response accuracy by 32%. Built distributed training orchestration services using Triton and CUDA optimizations, reducing inference latency by 28%. Designed experiment tracking and historical insights platforms for 3,000+ internal users with automated failure recovery and long-running workflow management. Implemented agentic planning/executor architectures using Python, PyTorch, and retrieval-augmented workflows to automate multi-stage experimentation, validation, and optimization. Optimized kernel execution pipelines using Triton, PyTorch Profiler, and quantization, improving training throughput by 25%. Built microservice-based orchestration integrated with private cloud infrastructure for scalable

Machine Learning Engineer at Accenture

December 1, 2020 - June 30, 2024

Developed Python-based demand forecasting pipelines using Spark, Databricks, and MLflow to process large-scale supply chain datasets, improving forecast accuracy by 32%. Built real-time anomaly detection services using Python, Kafka, and FastAPI for logistics event streams, reducing shipment disruption response latency by 41%. Designed inventory optimization models using OR-Tools, PySpark, and Azure ML, reducing operational planning costs by 28%. Engineered scalable ETL pipelines with PySpark, Delta Lake, Databricks, and SQL across ERP/warehouse/logistics data for enterprise-wide forecasting and planning. Implemented cloud-native microservices (FastAPI, Docker, Kubernetes) and deployed AI workloads on AWS/Azure with Terraform, CI/CD automation for provisioning and model deployment. Integrated enterprise systems (SAP, REST APIs, Azure Data Lake, AWS storage) for secure real-time synchronization. Created dashboards using React.js, Power BI, Grafana, and GraphQL APIs to visualize forecast