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AI/ML Engineer with 4.5+ years designing, deploying, and scaling production-grade machine learning and generative AI solutions across enterprise and cloud environments. Strong background in LLMs, RAG architectures, NLP, computer vision, and predictive analytics, with hands-on experience building autonomous agent systems and semantic search platforms. Proficient in end-to-end MLOps (training, deployment, monitoring, lifecycle management) using tools such as MLflow, Docker, Kubernetes, and cloud ML platforms across AWS, Azure, and GCP. Experienced with large-scale data processing using Spark, Snowflake, Kafka, and modern ETL frameworks; focused on delivering reliable, high-impact AI solutions that balance performance, cost, and real-world constraints.…AI/ML Engineer with 4.5+ years designing, deploying, and scaling production-grade machine learning and generative AI solutions across enterprise and cloud environments. Strong background in LLMs, RAG architectures, NLP, computer vision, and predictive analytics, with hands-on experience building autonomous agent systems and semantic search platforms. Proficient in end-to-end MLOps (training, deployment, monitoring, lifecycle management) using tools such as MLflow, Docker, Kubernetes, and cloud ML platforms across AWS, Azure, and GCP. Experienced with large-scale data processing using Spark, Snowflake, Kafka, and modern ETL frameworks; focused on delivering reliable, high-impact AI solutions that balance performance, cost, and real-world constraints.

AI/ML Engineer Yamin Divya

AI Engineer, Data Scientist, DevOps Developer

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AI/ML Engineer with 4.5+ years designing, deploying, and scaling production-grade machine learning and generative AI solutions across enterprise and cloud environments. Strong background in LLMs, RAG architectures, NLP, computer vision, and predictive analytics, with hands-on experience building autonomous agent systems and semantic search platforms. Proficient in end-to-end MLOps (training, deployment, monitoring, lifecycle management) using tools such as MLflow, Docker, Kubernetes, and cloud ML platforms across AWS, Azure, and GCP. Experienced with large-scale data processing using Spark, Snowflake, Kafka, and modern ETL frameworks; focused on delivering reliable, high-impact AI solutions that balance performance, cost, and real-world constraints.…AI/ML Engineer with 4.5+ years designing, deploying, and scaling production-grade machine learning and generative AI solutions across enterprise and cloud environments. Strong background in LLMs, RAG architectures, NLP, computer vision, and predictive analytics, with hands-on experience building autonomous agent systems and semantic search platforms. Proficient in end-to-end MLOps (training, deployment, monitoring, lifecycle management) using tools such as MLflow, Docker, Kubernetes, and cloud ML platforms across AWS, Azure, and GCP. Experienced with large-scale data processing using Spark, Snowflake, Kafka, and modern ETL frameworks; focused on delivering reliable, high-impact AI solutions that balance performance, cost, and real-world constraints.

Available to hire

AI/ML Engineer with 4.5+ years designing, deploying, and scaling production-grade machine learning and generative AI solutions across enterprise and cloud environments. Strong background in LLMs, RAG architectures, NLP, computer vision, and predictive analytics, with hands-on experience building autonomous agent systems and semantic search platforms.

Proficient in end-to-end MLOps (training, deployment, monitoring, lifecycle management) using tools such as MLflow, Docker, Kubernetes, and cloud ML platforms across AWS, Azure, and GCP. Experienced with large-scale data processing using Spark, Snowflake, Kafka, and modern ETL frameworks; focused on delivering reliable, high-impact AI solutions that balance performance, cost, and real-world constraints.

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Work Experience

AI/ML Engineer at Dell Technologies

February 1, 2024 - Present

Orchestrated production-grade RAG architectures using LangChain and LlamaIndex, integrating Snowflake data with large language models to deliver high-accuracy semantic search capabilities in Dell AI Factory infrastructure. Engineered specialized autonomous multi-agent systems via LangGraph and AutoGen, automating complex cross-functional engineering workflows and transitioning from static model deployments to robust, dynamic enterprise solutions. Optimized Hugging Face transformer deployments for ruggedized edge servers using LoRA and QLoRA to maintain peak performance under strict hardware and low-latency constraints. Standardized enterprise MLOps lifecycles by integrating MLflow with Azure ML Studio, using FastAPI and Docker to ensure consistent, containerized model serving alongside proactive real-time data drift monitoring systems. Built predictive maintenance models using PyTorch and XGBoost with Apache Spark for massive data processing to identify potential hardware failures be

ML Engineer at VMware

April 1, 2020 - August 31, 2022

Deployed production-scale predictive models using TensorFlow and Keras to optimize virtual machine resource allocation, reducing latency and improving hardware utilization during peak demand periods across cloud infrastructure. Designed end-to-end MLOps pipelines using Kubeflow and Docker to automate model training and versioning, enabling rapid deployment cycles with consistent performance monitoring for real-time anomaly detection systems. Implemented reusable feature stores/centralized variables for consistent inputs across different machine learning projects, reducing model development time by providing standardized pre-processed data. Applied statistical modeling and hyperparameter tuning using scikit-learn and Optuna to refine recommendation engines and deliver highly personalized product suggestions based on customer usage patterns.

Data Engineer at Company not specified

May 1, 2019 - March 31, 2020

Built robust ETL pipelines using Apache Airflow and Spark to process multi-terabyte datasets, ensuring seamless data flow from legacy on-premise systems into centralized AWS S3 data lakes for downstream analytics. Architected scalable data warehousing solutions using Snowflake and Redshift, improving query performance with advanced partitioning strategies and materialized views for near real-time Tableau reporting. Developed complex SQL transformations and dbt models to sanitize raw telemetry data, improving data quality and reliability for business intelligence initiatives while reducing manual cleaning work. Implemented automated data validation frameworks in Python to detect schema drift and null values early in ingestion pipelines. Streamlined resource allocation in Hadoop clusters by tuning YARN configurations and Hive queries, resulting in faster processing times for heavy batch jobs and reduced operational costs during peak data loads.