Generative AI & ML Engineer (Giant Models)

Vaishnavi Guntha

Gen AI ML Engineer

+1-925-***-**** *****.*.****@*****.*** Linkedin

Summary

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. Professional Experience

BlueCross BlueShield- Gen AI ML Engineer September 2023 - Present

● 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.

● Engineered data pipelines with Apache Spark, Kafka, Databricks, Delta Lake, and Snowflake, applying feature engineering, embeddings, and synthetic data generation for model training.

● Built and evaluated generative AI applications across text, image, and speech, using Whisper, TTS, diffusion models, RLHF, preference tuning, and multimodal fusion techniques.

● Deployed production-ready systems on Azure (ML, OpenAI, Cognitive Services), with strong focus on monitoring, observability, and cost optimization.

● Applied DevSecOps practices for AI: ethical AI principles, fairness, bias detection, interpretability (SHAP, LIME), and responsible AI frameworks for compliance and trustworthiness.

● Collaborated cross-functionally with product, data, and research teams to deliver enterprise copilots, recommendation engines, chatbots, and generative content platforms driving measurable business outcomes.

● Built and automated end-to-end MLOps pipelines with Kubeflow, MLflow, DVC, Airflow, Docker, Kubernetes, and Terraform, enabling reproducible training, model versioning, continuous integration (CI/CD) PayPal- AI ML Engineer October 2021 - May 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 checks using SHAP, LIME, and Responsible AI frameworks, improving trust and compliance of deployed systems.

● Collaborated with cross-functional teams to deliver end-to-end ML solutions (chatbots, recommendation engines, forecasting systems, anomaly detection) that drove measurable business impact. KPMG- ML Engineer March 2019 - July 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.

Skills

Programming & Scripting: Python, R, SQL, Java, C++ Machine Learning & Deep Learning: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, Keras Generative AI & NLP: Hugging Face Transformers, LangChain, LLaMA, GPT, Mistral, Stable Diffusion, CLIP, Whisper, RAG, LoRA/QLoRA, RLHF

MLOps & Deployment: MLflow, Kubeflow, DVC, Airflow, Docker, Kubernetes, Terraform, CI/CD, ONNX, TensorRT, OpenVINO Cloud & Platforms: AWS (SageMaker, Bedrock, Lambda), Azure (ML, OpenAI, Copilot Studio), GCP (Vertex AI, TPU Pods) Data Engineering: Apache Spark, Kafka, Databricks, Delta Lake, Snowflake, Pandas, NumPy Databases & Vector Stores: MySQL, PostgreSQL, MongoDB, FAISS, Pinecone, Weaviate, Milvus, ChromaDB Visualization & Analytics: Matplotlib, Plotly, Power BI, Tableau Responsible AI & Interpretability: SHAP, LIME, Fairlearn, Ethical AI frameworks Education

University of Texas at Dallas, Dallas, TX May 2023 Master of Science in Information Systems

Projects

Cloud POS Backend with AWS

● Developed POS-based backend application using AWS services and Implemented Java 11 with AWS Lambda to trigger end- point events going serverless

● Generated endpoint with API gateway, leveraged DynamoDB for data store and retrieval, cloud watch dashboard, CloudFormation, and AWS CDK to generate the above resources written in type script

● Backend service would help create and retrieve orders where the model classes are generated with SMITHY to have a universal model adaptation

Graduate School Admission Analysis (Python, SQL, R)

● Examined college admission dataset in Python and SQL, reduced prediction errors by 20% via R-Studio regression, and identified key admission factors

● Enhanced predictive accuracy for college admissions using linear and random forest models using Python, facilitating strategic decision-making

Certifications

AWS Certified Cloud Practitioner

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