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I am Sai Charan Reddy Gottam, an AI/ML Engineer with 4 years of experience building and deploying production-grade ML and Generative AI systems across Databricks and cloud platforms. I excel at turning complex business problems into secure, high-performance AI solutions using Databricks Lakehouse, Spark, MLflow, and modern cloud-native architectures, delivering measurable impact such as 30% faster model training, deployment cycles shortened from weeks to days, and 25% faster support resolution.…I am Sai Charan Reddy Gottam, an AI/ML Engineer with 4 years of experience building and deploying production-grade ML and Generative AI systems across Databricks and cloud platforms. I excel at turning complex business problems into secure, high-performance AI solutions using Databricks Lakehouse, Spark, MLflow, and modern cloud-native architectures, delivering measurable impact such as 30% faster model training, deployment cycles shortened from weeks to days, and 25% faster support resolution.

Sai Charan Reddy Gottam

AI Engineer, Data Scientist, AI Developer, +2

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I am Sai Charan Reddy Gottam, an AI/ML Engineer with 4 years of experience building and deploying production-grade ML and Generative AI systems across Databricks and cloud platforms. I excel at turning complex business problems into secure, high-performance AI solutions using Databricks Lakehouse, Spark, MLflow, and modern cloud-native architectures, delivering measurable impact such as 30% faster model training, deployment cycles shortened from weeks to days, and 25% faster support resolution.…I am Sai Charan Reddy Gottam, an AI/ML Engineer with 4 years of experience building and deploying production-grade ML and Generative AI systems across Databricks and cloud platforms. I excel at turning complex business problems into secure, high-performance AI solutions using Databricks Lakehouse, Spark, MLflow, and modern cloud-native architectures, delivering measurable impact such as 30% faster model training, deployment cycles shortened from weeks to days, and 25% faster support resolution.

Available to hire

I am Sai Charan Reddy Gottam, an AI/ML Engineer with 4 years of experience building and deploying production-grade ML and Generative AI systems across Databricks and cloud platforms.
I excel at turning complex business problems into secure, high-performance AI solutions using Databricks Lakehouse, Spark, MLflow, and modern cloud-native architectures, delivering measurable impact such as 30% faster model training, deployment cycles shortened from weeks to days, and 25% faster support resolution.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at Databricks

August 1, 2024 - Present

Designed and delivered enterprise-grade Generative AI solutions using Hugging Face Transformers, OpenAI APIs, and AION, fine-tuning large language models (LLMs) for production chatbots and reducing support ticket resolution time by 25%. Architected and deployed scalable ML models on the Databricks Lakehouse leveraging Python, PySpark, Delta Lake, and MLflow, cutting model training runtime by 30% while improving experiment tracking, reproducibility, and governance. Built Retrieval-Augmented Generation (RAG) pipelines by integrating LLMs with LangChain and Pinecone vector databases, enabling semantic search and enterprise knowledge retrieval while significantly reducing manual information lookup time. Implemented end-to-end MLOps pipelines using GitHub Actions, Docker, and Kubernetes, automating CI/CD for AI/ML models and accelerating deployment cycles from 2 weeks to 5 days. Optimized data ingestion, feature engineering, and distributed training workflows using Spark-based pipelines, im

Machine Learning Engineer at Cognizant

January 1, 2021 - July 1, 2023

Developed, trained, and optimized production-grade ML/DL models using Python, TensorFlow, PyTorch, and scikit-learn, improving model accuracy by 25% while reducing inference latency on large-scale datasets. Architected and automated end-to-end ML pipelines using Apache Airflow, MLflow, and Kubeflow, covering data ingestion, feature engineering, model training, experiment tracking, versioning, and deployment—cutting manual effort by 60%. Designed, fine-tuned, and evaluated advanced ML/DL architectures including Transformers, CNNs, and RNNs for NLP, computer vision, and predictive analytics, driving 20–30% gains in precision and recall across multiple production use cases. Deployed and scaled real-time and batch inference models on AWS SageMaker, Azure Machine Learning, and GCP Vertex AI, supporting low-latency, high-availability workloads in production environments. Processed and engineered large-scale structured and unstructured data using Apache Spark, Databricks, and distributed