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I am Lokesh Prasad Kukatla, an AI/ML engineer focused on designing and deploying scalable AI solutions across healthcare, insurance, and enterprise platforms. I specialize in Generative AI, NLP, computer vision, and large language models, and I enjoy turning complex data into practical automation that improves accuracy and user experience. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face, with strong expertise in architecting MLOps pipelines, automating deployments, and maintaining HIPAA-compliant governance. I’ve delivered cloud-native AI apps on AWS, Azure, and GCP using Kubernetes, Snowflake, and modern tooling, always aiming for production-grade reliability and measurable business impact.…I am Lokesh Prasad Kukatla, an AI/ML engineer focused on designing and deploying scalable AI solutions across healthcare, insurance, and enterprise platforms. I specialize in Generative AI, NLP, computer vision, and large language models, and I enjoy turning complex data into practical automation that improves accuracy and user experience. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face, with strong expertise in architecting MLOps pipelines, automating deployments, and maintaining HIPAA-compliant governance. I’ve delivered cloud-native AI apps on AWS, Azure, and GCP using Kubernetes, Snowflake, and modern tooling, always aiming for production-grade reliability and measurable business impact.

Lokesh Prasad Kukatla

AI Engineer, Data Scientist, Full Stack Developer, +7

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I am Lokesh Prasad Kukatla, an AI/ML engineer focused on designing and deploying scalable AI solutions across healthcare, insurance, and enterprise platforms. I specialize in Generative AI, NLP, computer vision, and large language models, and I enjoy turning complex data into practical automation that improves accuracy and user experience. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face, with strong expertise in architecting MLOps pipelines, automating deployments, and maintaining HIPAA-compliant governance. I’ve delivered cloud-native AI apps on AWS, Azure, and GCP using Kubernetes, Snowflake, and modern tooling, always aiming for production-grade reliability and measurable business impact.…I am Lokesh Prasad Kukatla, an AI/ML engineer focused on designing and deploying scalable AI solutions across healthcare, insurance, and enterprise platforms. I specialize in Generative AI, NLP, computer vision, and large language models, and I enjoy turning complex data into practical automation that improves accuracy and user experience. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face, with strong expertise in architecting MLOps pipelines, automating deployments, and maintaining HIPAA-compliant governance. I’ve delivered cloud-native AI apps on AWS, Azure, and GCP using Kubernetes, Snowflake, and modern tooling, always aiming for production-grade reliability and measurable business impact.

Available to hire

I am Lokesh Prasad Kukatla, an AI/ML engineer focused on designing and deploying scalable AI solutions across healthcare, insurance, and enterprise platforms. I specialize in Generative AI, NLP, computer vision, and large language models, and I enjoy turning complex data into practical automation that improves accuracy and user experience.

I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face, with strong expertise in architecting MLOps pipelines, automating deployments, and maintaining HIPAA-compliant governance. I’ve delivered cloud-native AI apps on AWS, Azure, and GCP using Kubernetes, Snowflake, and modern tooling, always aiming for production-grade reliability and measurable business impact.

Skills

Experience Level

Expert

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Expert

Language

English

Fluent

Work Experience

AI Engineer at Cigna Healthcare Group

February 1, 2025 - November 7, 2025

Designed and deployed an AI-powered virtual health assistant leveraging LLMs and NLP to automate customer queries, significantly improving support efficiency. Fine-tuned Hugging Face transformer models on proprietary data to deliver context-aware, HIPAA-compliant conversational accuracy. Integrated the assistant into myCigna web and mobile platforms via Azure OpenAI Services and Kubernetes-based microservices, enabling scalable, low-latency responses for thousands of users. Implemented an MLOps pipeline with CI/CD, automated retraining, and monitoring, reducing update cycles. Built dashboards to monitor user engagement and satisfaction, guiding continuous improvements. Ensured cloud-native architecture with HIPAA governance across AI workflows, collaborating with cross-functional teams to align AI initiatives with business goals.

Machine Learning Engineer at Mphasis

July 1, 2023 - July 1, 2023

Built AI-driven computer vision and NLP systems for manufacturing and insurance clients using TensorFlow, Keras, scikit-learn, and OpenCV, improving automation accuracy by over 35%. Developed a CNN-based visual defect detection model achieving 94% accuracy. Created a fraud-detection ML pipeline using XGBoost and Random Forest. Designed and deployed AWS-based ML infrastructure (SageMaker, Lambda, EC2) to automate real-time model inference and data monitoring. Implemented explainable AI (SHAP, What-If Analysis) to ensure transparency and auditor trust in fraud detection outcomes. Built an AI-powered due diligence platform leveraging transformer-based NLP models and Elasticsearch, improving research speed by 90%. Automated data ingestion and processing pipelines across multiple sources using pandas, AWS, and Python, cutting ETL time by 35%. Collaborated with data engineers and analysts to scale ML operations for global clients.

AI Engineer at Cigna Healthcare Group, USA

February 1, 2025 - November 7, 2025

Designed and deployed an AI-powered virtual health assistant using LLMs and NLP, automating customer queries and improving support efficiency by 40%. Fine-tuned HuggingFace Transformer models on proprietary datasets to achieve context-aware and HIPAA-compliant conversational accuracy. Integrated the assistant into myCigna web and mobile platforms via Azure OpenAI Services and Kubernetes microservices, enabling low-latency, scalable responses for thousands of users. Implemented an MLOps pipeline with CI/CD, automated retraining, and monitoring, reducing update time by 30%. Built dashboards to track user engagement and satisfaction, and deployed secure HIPAA-governed cloud-native AI workflows across all AI initiatives.

Machine Learning Engineer at Mphasis, India

July 1, 2023 - July 1, 2023

Built AI-driven computer vision and NLP systems for manufacturing and insurance clients using TensorFlow, Keras, scikit-learn, and OpenCV, improving automation accuracy by over 35%. Developed a visual defect detection CNN achieving 94% accuracy and a fraud detection ML pipeline using XGBoost and Random Forest, achieving 98% accuracy. Designed and deployed AWS-based ML infrastructure (SageMaker, Lambda, EC2) to automate real-time model inference and data monitoring. Implemented SHAP explainability and What-If analyses for transparency, and built an AI-powered due diligence platform leveraging transformer-based NLP and Elasticsearch, improving research speed by 90%. Automated data ingestion and processing pipelines across multiple sources, cutting ETL time by 35%.

Machine Learning Engineer at Mphasis

August 1, 2019 - July 1, 2023

Built AI-driven computer vision and NLP solutions using TensorFlow, Keras, and OpenCV to boost automation accuracy by 35% across manufacturing inspection and insurance document workflows. Developed a CNN-based defect detection model achieving 94% accuracy, reducing manual inspection and accelerating QA pipelines. Engineered an end-to-end fraud detection pipeline with XGBoost and Random Forest, achieving 98% predictive accuracy for high-volume transactions. Designed AWS-based ML infrastructure (SageMaker, Lambda, EC2) enabling real-time inference across multiple deployments. Implemented explainability using SHAP and What-If Analysis to enhance transparency for regulated fraud detection. Built an NLP-driven due-diligence platform using transformer models and Elasticsearch to accelerate research workflows by 90% for global enterprise users. Automated data ingestion pipelines with pandas and Python on AWS, reducing ETL time by 35% and improving data freshness. Collaborated with engineers a