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Hi, I’m Lokesh Kasamneni, an AI & ML Engineer with 5 years of experience building production-grade ML, DL, and LLM systems across IT and Healthcare. I specialize in Generative AI, LLM workflows, Retrieval-Augmented Generation (RAG) pipelines, semantic search, vector databases, and multimodal AI architectures, delivering scalable, high-performance AI solutions. I bring end-to-end expertise across the ML lifecycle—from data ingestion and feature engineering to model development, evaluation, deployment, and monitoring. I’ve deployed cloud-native inference services on AWS SageMaker, Azure ML, and GCP Vertex AI, built MLOps automation, and collaborated with data, product, and clinical teams to drive measurable improvements in KPIs.…Hi, I’m Lokesh Kasamneni, an AI & ML Engineer with 5 years of experience building production-grade ML, DL, and LLM systems across IT and Healthcare. I specialize in Generative AI, LLM workflows, Retrieval-Augmented Generation (RAG) pipelines, semantic search, vector databases, and multimodal AI architectures, delivering scalable, high-performance AI solutions. I bring end-to-end expertise across the ML lifecycle—from data ingestion and feature engineering to model development, evaluation, deployment, and monitoring. I’ve deployed cloud-native inference services on AWS SageMaker, Azure ML, and GCP Vertex AI, built MLOps automation, and collaborated with data, product, and clinical teams to drive measurable improvements in KPIs.

Lokesh Kasamneni

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

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Hi, I’m Lokesh Kasamneni, an AI & ML Engineer with 5 years of experience building production-grade ML, DL, and LLM systems across IT and Healthcare. I specialize in Generative AI, LLM workflows, Retrieval-Augmented Generation (RAG) pipelines, semantic search, vector databases, and multimodal AI architectures, delivering scalable, high-performance AI solutions. I bring end-to-end expertise across the ML lifecycle—from data ingestion and feature engineering to model development, evaluation, deployment, and monitoring. I’ve deployed cloud-native inference services on AWS SageMaker, Azure ML, and GCP Vertex AI, built MLOps automation, and collaborated with data, product, and clinical teams to drive measurable improvements in KPIs.…Hi, I’m Lokesh Kasamneni, an AI & ML Engineer with 5 years of experience building production-grade ML, DL, and LLM systems across IT and Healthcare. I specialize in Generative AI, LLM workflows, Retrieval-Augmented Generation (RAG) pipelines, semantic search, vector databases, and multimodal AI architectures, delivering scalable, high-performance AI solutions. I bring end-to-end expertise across the ML lifecycle—from data ingestion and feature engineering to model development, evaluation, deployment, and monitoring. I’ve deployed cloud-native inference services on AWS SageMaker, Azure ML, and GCP Vertex AI, built MLOps automation, and collaborated with data, product, and clinical teams to drive measurable improvements in KPIs.

Available to hire

Hi, I’m Lokesh Kasamneni, an AI & ML Engineer with 5 years of experience building production-grade ML, DL, and LLM systems across IT and Healthcare. I specialize in Generative AI, LLM workflows, Retrieval-Augmented Generation (RAG) pipelines, semantic search, vector databases, and multimodal AI architectures, delivering scalable, high-performance AI solutions.

I bring end-to-end expertise across the ML lifecycle—from data ingestion and feature engineering to model development, evaluation, deployment, and monitoring. I’ve deployed cloud-native inference services on AWS SageMaker, Azure ML, and GCP Vertex AI, built MLOps automation, and collaborated with data, product, and clinical teams to drive measurable improvements in KPIs.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI & ML Engineer at CVS Health

February 1, 2024 - Present

Designed and deployed end-to-end ML and LLM pipelines for healthcare applications, including Retrieval-Augmented Generation (RAG) workflows, vector search, and semantic search using LangChain, LlamaIndex, FAISS, Pinecone, and OpenAI APIs. Fine-tuned Hugging Face transformer models for NLP and multimodal AI tasks, improving inference accuracy by 20%. Built scalable inference services on AWS SageMaker, Azure ML, and GCP Vertex AI with latency under 150 ms and reduced cloud costs by ~25%. Applied reinforcement learning and optimization with ONNX, quantization, and pruning to boost throughput by ~50%. Developed MLflow-based MLOps pipelines and automated model drift detection dashboards, reducing production incidents by ~40%. Collaborated with data engineering, clinical, and product teams to integrate AI into healthcare workflows, boosting operational efficiency by ~25%. Designed and maintained ETL pipelines for structured EHR, claims, and lab data ensuring HIPAA-compliant processing. Assis

AI/ML Intern at Cigna Health Care

August 1, 2023 - January 31, 2024

Assisted in building ML pipelines for clinical datasets (EHR, HL7, imaging), improving data quality by 28%. Implemented basic semantic search solutions using FAISS and Pinecone for clinical documents, enhancing retrieval efficiency. Performed feature engineering, data preprocessing, and exploratory data analysis to support predictive modeling pipelines for patient risk scoring. Created dashboards and EDA reports for healthcare analysts, enabling faster insights for clinical decision-making. Assisted in model evaluation and experimentation for multimodal AI tasks, ensuring HIPAA-compliant data handling and workflow documentation. Supported deployment of ML models using Docker and cloud platforms.

Jr. ML Engineer at Lex Nimble Solutions Limited

March 1, 2019 - April 1, 2022

Built and trained ML models for classification and regression workflows using TensorFlow, PyTorch, and Scikit-learn, improving predictive accuracy. Developed automated ETL and feature engineering pipelines with Python, Pandas, and SQL, reducing data preparation time by 30%. Conducted data cleaning, preprocessing, and exploratory data analysis (EDA) to improve dataset quality and consistency. Deployed ML models via Flask APIs and containerized with Docker, supporting team-level production experiments. Assisted in orchestrating ML workflows on Kubernetes and deploying models to AWS and Azure, improving deployment efficiency. Supported CI/CD integration, model versioning, and monitoring workflows, ensuring smooth updates and reproducibility. Collaborated with cross-functional teams to embed ML solutions into operational pipelines and analytics workflows. Performed hyperparameter tuning and model validation, analyzing predictive performance to ensure robust and reliable ML models. Document