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I am Mahesh Krishna Bandla, an AI Engineer based in the United States, with 4.5+ years of experience building production-grade large language models for healthcare and insurance domains, enabling document intelligence, reasoning, and NLP-driven automation. I specialize in end-to-end AI systems, including LLM fine-tuning, model distillation, and transformer architectures, with a focus on scalable production deployment. I also design LLM-powered search and retrieval systems, RAG-style architectures, secure AI service layers on Azure with CI/CD, and autonomous agents for enterprise automation.…I am Mahesh Krishna Bandla, an AI Engineer based in the United States, with 4.5+ years of experience building production-grade large language models for healthcare and insurance domains, enabling document intelligence, reasoning, and NLP-driven automation. I specialize in end-to-end AI systems, including LLM fine-tuning, model distillation, and transformer architectures, with a focus on scalable production deployment. I also design LLM-powered search and retrieval systems, RAG-style architectures, secure AI service layers on Azure with CI/CD, and autonomous agents for enterprise automation.

Mahesh Krishna Bandla

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

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I am Mahesh Krishna Bandla, an AI Engineer based in the United States, with 4.5+ years of experience building production-grade large language models for healthcare and insurance domains, enabling document intelligence, reasoning, and NLP-driven automation. I specialize in end-to-end AI systems, including LLM fine-tuning, model distillation, and transformer architectures, with a focus on scalable production deployment. I also design LLM-powered search and retrieval systems, RAG-style architectures, secure AI service layers on Azure with CI/CD, and autonomous agents for enterprise automation.…I am Mahesh Krishna Bandla, an AI Engineer based in the United States, with 4.5+ years of experience building production-grade large language models for healthcare and insurance domains, enabling document intelligence, reasoning, and NLP-driven automation. I specialize in end-to-end AI systems, including LLM fine-tuning, model distillation, and transformer architectures, with a focus on scalable production deployment. I also design LLM-powered search and retrieval systems, RAG-style architectures, secure AI service layers on Azure with CI/CD, and autonomous agents for enterprise automation.

Available to hire

I am Mahesh Krishna Bandla, an AI Engineer based in the United States, with 4.5+ years of experience building production-grade large language models for healthcare and insurance domains, enabling document intelligence, reasoning, and NLP-driven automation.

I specialize in end-to-end AI systems, including LLM fine-tuning, model distillation, and transformer architectures, with a focus on scalable production deployment. I also design LLM-powered search and retrieval systems, RAG-style architectures, secure AI service layers on Azure with CI/CD, and autonomous agents for enterprise automation.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

AI Engineer at Elevance Health

March 1, 2024 - Present

Architected and deployed domain-specific LLMs for healthcare and insurance document intelligence; performed model distillation and fine-tuning with Hugging Face and Azure distributed training; designed LLM-powered semantic search and RAG-style retrieval; built Model Context Protocol (MCP) based AI service layers; developed end-to-end MLOps pipelines on Azure; created autonomous AI agents with memory and multi-step reasoning; implemented real-time streaming with Apache Kafka; established CI/CD pipelines with Azure DevOps and SonarQube; created automated evaluation and testing frameworks to improve reliability and throughput.

Full Stack Software Engineer at Accenture

August 1, 2020 - August 31, 2023

Developed scalable RESTful APIs using Python, Flask, and SQLAlchemy; built full-stack applications with React and Flask, connecting to PostgreSQL/SQL Server; designed optimized relational schemas, implemented API versioning and RBAC; established CI/CD pipelines with Jenkins and Git; implemented a Change Data Capture (CDC) approach for near real-time analytics; containerized deployments with Docker and Nginx on AWS EC2; delivered automated unit/integration tests with PyTest.