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I'm Thanshith Kannedari, an AI/ML Engineer with 4+ years of experience building production-scale AI/ML systems, generative AI applications, and cloud-native ML platforms across AWS and GCP. I design end-to-end ML workflows, from data ingestion and retrieval to inference, evaluation, monitoring, and deployment, leveraging Python, PyTorch, TensorFlow, FastAPI, Kubernetes, and Vertex AI. I thrive on turning complex data into reliable, scalable AI solutions and enjoy collaborating with cross-functional teams to improve model performance, retrieval quality, and operational reliability, delivering measurable impact at enterprise scale.…I'm Thanshith Kannedari, an AI/ML Engineer with 4+ years of experience building production-scale AI/ML systems, generative AI applications, and cloud-native ML platforms across AWS and GCP. I design end-to-end ML workflows, from data ingestion and retrieval to inference, evaluation, monitoring, and deployment, leveraging Python, PyTorch, TensorFlow, FastAPI, Kubernetes, and Vertex AI. I thrive on turning complex data into reliable, scalable AI solutions and enjoy collaborating with cross-functional teams to improve model performance, retrieval quality, and operational reliability, delivering measurable impact at enterprise scale.

Thanshith Kannedari

AI Engineer, Data Scientist, Full Stack Developer, +2

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I'm Thanshith Kannedari, an AI/ML Engineer with 4+ years of experience building production-scale AI/ML systems, generative AI applications, and cloud-native ML platforms across AWS and GCP. I design end-to-end ML workflows, from data ingestion and retrieval to inference, evaluation, monitoring, and deployment, leveraging Python, PyTorch, TensorFlow, FastAPI, Kubernetes, and Vertex AI. I thrive on turning complex data into reliable, scalable AI solutions and enjoy collaborating with cross-functional teams to improve model performance, retrieval quality, and operational reliability, delivering measurable impact at enterprise scale.…I'm Thanshith Kannedari, an AI/ML Engineer with 4+ years of experience building production-scale AI/ML systems, generative AI applications, and cloud-native ML platforms across AWS and GCP. I design end-to-end ML workflows, from data ingestion and retrieval to inference, evaluation, monitoring, and deployment, leveraging Python, PyTorch, TensorFlow, FastAPI, Kubernetes, and Vertex AI. I thrive on turning complex data into reliable, scalable AI solutions and enjoy collaborating with cross-functional teams to improve model performance, retrieval quality, and operational reliability, delivering measurable impact at enterprise scale.

Available to hire

I’m Thanshith Kannedari, an AI/ML Engineer with 4+ years of experience building production-scale AI/ML systems, generative AI applications, and cloud-native ML platforms across AWS and GCP. I design end-to-end ML workflows, from data ingestion and retrieval to inference, evaluation, monitoring, and deployment, leveraging Python, PyTorch, TensorFlow, FastAPI, Kubernetes, and Vertex AI.

I thrive on turning complex data into reliable, scalable AI solutions and enjoy collaborating with cross-functional teams to improve model performance, retrieval quality, and operational reliability, delivering measurable impact at enterprise scale.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Work Experience

Machine Learning Engineer at Alphabet

March 1, 2015 - Present

Owned end-to-end development of enterprise AI platforms, designing data ingestion, retrieval, inference, evaluation, monitoring, and deployment workflows across GCP environments. Built scalable ML and Retrieval-Augmented Generation (RAG) pipelines using Gemini, Vertex AI, FastAPI, BigQuery, and GKE, supporting enterprise knowledge discovery applications. Improved response accuracy by 34% and reduced hallucination rates by 29% through hybrid retrieval, reranking models, metadata filtering, and evaluation-driven optimization. Architected distributed AI microservices with FastAPI, Redis, Cloud SQL, and Pub/Sub to enable scalable model serving and workflow orchestration. Reduced p95 inference latency by 41% and lowered operational costs by 27% through caching strategies, autoscaling infrastructure, and retrieval optimization. Built data ingestion and preprocessing frameworks with Dataflow, Firestore, Pub/Sub, and Document AI for enterprise content processing. Established model evaluation a

Software Engineer at Accenture

April 1, 2011 - June 1, 2024

Developed production-grade AI applications using Python, FastAPI, Amazon Bedrock, PostgreSQL, Redis, and Pinecone, enabling scalable conversational AI solutions across business teams. Built retrieval and semantic search systems using LangChain, Amazon Kendra, OpenAI embeddings, and Pinecone to improve enterprise knowledge discovery capabilities. Designed secure backend services using FastAPI, JWT, OAuth2, RBAC, and Azure AD SSO, ensuring compliant access across enterprise AI platforms. Improved conversational response latency by 41% through Redis caching, asynchronous processing, optimized retrieval execution, and efficient Bedrock inference orchestration. Developed large-scale document ingestion and preprocessing pipelines using AWS Lambda, S3, Apache Tika, pandas, and PDF processing frameworks. Built containerized cloud-native services using Docker, Kubernetes, Amazon EKS, Helm, and Terraform, supporting scalable deployment and infrastructure automation workflows. Implemented observa