Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

AI/ML Engineer with 4+ years of experience building and deploying production-grade machine learning, deep learning, and Generative AI solutions for enterprise applications. Expertise includes LLM-powered systems, Retrieval-Augmented Generation (RAG), semantic search, and transformer-based models using Python and AWS. Experienced in end-to-end ML pipelines and MLOps using Docker, Kubernetes, FastAPI, MLflow, and CI/CD to reduce latency, improve retrieval quality, and ensure model reliability through monitoring and drift detection.…AI/ML Engineer with 4+ years of experience building and deploying production-grade machine learning, deep learning, and Generative AI solutions for enterprise applications. Expertise includes LLM-powered systems, Retrieval-Augmented Generation (RAG), semantic search, and transformer-based models using Python and AWS. Experienced in end-to-end ML pipelines and MLOps using Docker, Kubernetes, FastAPI, MLflow, and CI/CD to reduce latency, improve retrieval quality, and ensure model reliability through monitoring and drift detection.

Ganesh Chedula

AI Engineer, Data Scientist, AI Developer, +1





AI/ML Engineer with 4+ years of experience building and deploying production-grade machine learning, deep learning, and Generative AI solutions for enterprise applications. Expertise includes LLM-powered systems, Retrieval-Augmented Generation (RAG), semantic search, and transformer-based models using Python and AWS. Experienced in end-to-end ML pipelines and MLOps using Docker, Kubernetes, FastAPI, MLflow, and CI/CD to reduce latency, improve retrieval quality, and ensure model reliability through monitoring and drift detection.…AI/ML Engineer with 4+ years of experience building and deploying production-grade machine learning, deep learning, and Generative AI solutions for enterprise applications. Expertise includes LLM-powered systems, Retrieval-Augmented Generation (RAG), semantic search, and transformer-based models using Python and AWS. Experienced in end-to-end ML pipelines and MLOps using Docker, Kubernetes, FastAPI, MLflow, and CI/CD to reduce latency, improve retrieval quality, and ensure model reliability through monitoring and drift detection.

Available to hire

AI/ML Engineer with 4+ years of experience building and deploying production-grade machine learning, deep learning, and Generative AI solutions for enterprise applications. Expertise includes LLM-powered systems, Retrieval-Augmented Generation (RAG), semantic search, and transformer-based models using Python and AWS.

Experienced in end-to-end ML pipelines and MLOps using Docker, Kubernetes, FastAPI, MLflow, and CI/CD to reduce latency, improve retrieval quality, and ensure model reliability through monitoring and drift detection.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

AI/ML Engineer at Teledyne Technologies Inc.

January 1, 2025 - Present

Architected an LLM-based RAG system for enterprise search using vector embeddings and semantic retrieval pipelines, improving information retrieval efficiency by 30% across 500K+ documents. Engineered end-to-end ML pipelines and scalable AI microservices on AWS using FastAPI and Docker, reducing inference latency by 28% in production. Designed transformer-based embedding models to enhance semantic similarity search accuracy and improve enterprise knowledge discovery. Implemented prompt engineering and LLM optimization to improve response quality, relevance, and alignment. Built model monitoring, evaluation, and drift detection to maintain stability and reliability. Collaborated cross-functionally to integrate AI into business workflows, improving automation efficiency and reducing manual processing effort by 30%. Developed reusable data preprocessing and embedding pipelines for consistent quality across multiple AI/LLM applications.

Machine Learning Engineer at Colt Technology Services

November 1, 2020 - July 31, 2023

Built predictive machine learning and statistical models using scikit-learn, improving overall model accuracy by 24% through feature engineering and hyperparameter tuning. Implemented NLP-based classification systems for sentiment analysis and intent detection to automate customer support workflows, improving operational efficiency by 38%. Designed recommendation systems using collaborative filtering and user behavior analysis to increase engagement, personalization quality, and retention. Created end-to-end data science pipelines using SQL, Pandas, and ETL workflows to ensure clean and high-quality datasets. Deployed ML models using Flask APIs and Docker containers for scalable production-grade deployment, improving reliability and integration with enterprise applications.