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Innovative Generative AI & Machine Learning Engineer with expertise in designing, training, and deploying large-scale ML models, including transformers, diffusion models, and multimodal architectures. Skilled in LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and optimization for performance and scalability. Strong background in MLOps, model deployment, and cloud-native AI infrastructure with hands-on experience across PyTorch, TensorFlow, Hugging Face, and vector databases. Adept at leveraging data pipelines, feature engineering, and distributed training to deliver production-ready AI systems. Passionate about advancing GenAI applications such as text generation, image synthesis, and conversational AI while ensuring fairness, interpretability, and efficiency.…Innovative Generative AI & Machine Learning Engineer with expertise in designing, training, and deploying large-scale ML models, including transformers, diffusion models, and multimodal architectures. Skilled in LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and optimization for performance and scalability. Strong background in MLOps, model deployment, and cloud-native AI infrastructure with hands-on experience across PyTorch, TensorFlow, Hugging Face, and vector databases. Adept at leveraging data pipelines, feature engineering, and distributed training to deliver production-ready AI systems. Passionate about advancing GenAI applications such as text generation, image synthesis, and conversational AI while ensuring fairness, interpretability, and efficiency.

Vaishnavi Guntha

AI Engineer, Data Scientist, AI Prompt Engineer, +2

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Innovative Generative AI & Machine Learning Engineer with expertise in designing, training, and deploying large-scale ML models, including transformers, diffusion models, and multimodal architectures. Skilled in LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and optimization for performance and scalability. Strong background in MLOps, model deployment, and cloud-native AI infrastructure with hands-on experience across PyTorch, TensorFlow, Hugging Face, and vector databases. Adept at leveraging data pipelines, feature engineering, and distributed training to deliver production-ready AI systems. Passionate about advancing GenAI applications such as text generation, image synthesis, and conversational AI while ensuring fairness, interpretability, and efficiency.…Innovative Generative AI & Machine Learning Engineer with expertise in designing, training, and deploying large-scale ML models, including transformers, diffusion models, and multimodal architectures. Skilled in LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and optimization for performance and scalability. Strong background in MLOps, model deployment, and cloud-native AI infrastructure with hands-on experience across PyTorch, TensorFlow, Hugging Face, and vector databases. Adept at leveraging data pipelines, feature engineering, and distributed training to deliver production-ready AI systems. Passionate about advancing GenAI applications such as text generation, image synthesis, and conversational AI while ensuring fairness, interpretability, and efficiency.

Available to hire

Innovative Generative AI & Machine Learning Engineer with expertise in designing, training, and deploying large-scale ML models, including transformers, diffusion models, and multimodal architectures. Skilled in LLM fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and optimization for performance and scalability.

Strong background in MLOps, model deployment, and cloud-native AI infrastructure with hands-on experience across PyTorch, TensorFlow, Hugging Face, and vector databases. Adept at leveraging data pipelines, feature engineering, and distributed training to deliver production-ready AI systems. Passionate about advancing GenAI applications such as text generation, image synthesis, and conversational AI while ensuring fairness, interpretability, and efficiency.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Gen AI ML Engineer at BlueCross BlueShield

September 1, 2023 - November 5, 2025

Designed, fine-tuned, and deployed Large Language Models (LLMs) and multimodal architectures (GPT, LLaMA, Falcon, Mistral, Stable Diffusion, CLIP) using PyTorch, TensorFlow, Hugging Face Transformers, LangChain, and Ray. Implemented retrieval-augmented generation (RAG) solutions with vector databases (FAISS) and knowledge-grounding pipelines for enterprise-grade search and conversational AI. Built scalable MLOps workflows with Kubeflow, MLflow, DVC, Airflow, Docker, Kubernetes, and Terraform, ensuring reproducibility, model versioning, and automated CI/CD pipelines. Optimized model efficiency through quantization (ONNX, TensorRT, Intel OpenVINO), pruning, distillation (LoRA, QLoRA, PEFT), and distributed training (DeepSpeed, Horovod, Accelerate), reducing latency and cloud costs. Developed copilots and custom AI assistants using Copilot Studio, Azure OpenAI, LangChain, Semantic Kernel, and integrated them with Microsoft 365, Teams, Slack, and enterprise APIs for workflow automation. En

AI ML Engineer at PayPal

May 1, 2023 - May 1, 2023

Designed and deployed machine learning models (classification, regression, NLP, CV, recommendation systems) using Python, PyTorch, TensorFlow, Scikit-learn, XGBoost, and LightGBM to solve real-world business problems. Built robust data pipelines with Apache Spark, Kafka, Airflow, Databricks, and Snowflake, ensuring scalable ETL, feature engineering, and real-time streaming for ML workloads. Implemented MLOps practices with MLflow, Kubeflow, Docker, Kubernetes, and Terraform to streamline model training, versioning, CI/CD, and monitoring in production environments. Optimized models for scalability and efficiency using distributed training (Horovod, Ray, DeepSpeed), hyperparameter tuning (Optuna, Ray Tune), and model compression (quantization, pruning, distillation). Deployed AI solutions on AWS SageMaker, integrating APIs, microservices, and serverless architectures (Lambda, Cloud Functions) for real-time inference. Conducted model explainability, interpretability, and bias/fairness che

ML Engineer at KPMG

July 1, 2021 - July 1, 2021

Developed, trained, and deployed machine learning models for NLP, computer vision, and predictive analytics using Python, PyTorch, TensorFlow, and Scikit-learn. Designed and maintained scalable data pipelines with Airflow, Spark, Kafka, and Databricks, ensuring efficient ETL, feature engineering, and real-time data processing. Implemented MLOps workflows with MLflow, Kubeflow, Docker, Kubernetes, and Terraform, enabling reproducible training, model versioning, CI/CD automation, and production monitoring. Optimized model performance through hyperparameter tuning. Deployed production-ready ML services on AWS, integrating APIs and microservices for real-time inference. Applied model explainability and fairness techniques to improve transparency, interpretability, and compliance of deployed systems. Partnered with cross-functional teams to deliver recommendation systems, anomaly detection pipelines, forecasting models, driving measurable business impact.