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I am a Generative AI & AI/ML Engineer with over five years of experience building advanced agentic AI systems involving multi-agent workflows, tool-using LLMs, and stateful memory. I specialize in retrieval-augmented generation (RAG), semantic search, and orchestrating LLMs with LangChain, LangGraph, and OpenAI Agents SDK, focusing on creating reasoning agents with dynamic tool selection and automated decision-making. I have extensive experience deploying scalable AI systems on cloud platforms like Azure, AWS, and GCP, utilizing MLOps tools to maintain model governance and compliance. My background includes a strong foundation in machine learning techniques such as risk modeling, telematics underwriting, and sentiment analysis using frameworks like XGBoost and PySpark. I am proficient in fine-tuning open-weight LLMs via LoRA and PEFT techniques and integrating human-in-the-loop frameworks to improve model precision and reliability. I enjoy collaborating cross-functionally to embed Generative AI capabilities into enterprise workflows, always striving to deliver adaptive, efficient, and context-aware AI solutions.…I am a Generative AI & AI/ML Engineer with over five years of experience building advanced agentic AI systems involving multi-agent workflows, tool-using LLMs, and stateful memory. I specialize in retrieval-augmented generation (RAG), semantic search, and orchestrating LLMs with LangChain, LangGraph, and OpenAI Agents SDK, focusing on creating reasoning agents with dynamic tool selection and automated decision-making. I have extensive experience deploying scalable AI systems on cloud platforms like Azure, AWS, and GCP, utilizing MLOps tools to maintain model governance and compliance. My background includes a strong foundation in machine learning techniques such as risk modeling, telematics underwriting, and sentiment analysis using frameworks like XGBoost and PySpark. I am proficient in fine-tuning open-weight LLMs via LoRA and PEFT techniques and integrating human-in-the-loop frameworks to improve model precision and reliability. I enjoy collaborating cross-functionally to embed Generative AI capabilities into enterprise workflows, always striving to deliver adaptive, efficient, and context-aware AI solutions.

Sainikhitha Reddy Bhoompelly

AI Engineer, Developer, Data Scientist, +1

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I am a Generative AI & AI/ML Engineer with over five years of experience building advanced agentic AI systems involving multi-agent workflows, tool-using LLMs, and stateful memory. I specialize in retrieval-augmented generation (RAG), semantic search, and orchestrating LLMs with LangChain, LangGraph, and OpenAI Agents SDK, focusing on creating reasoning agents with dynamic tool selection and automated decision-making. I have extensive experience deploying scalable AI systems on cloud platforms like Azure, AWS, and GCP, utilizing MLOps tools to maintain model governance and compliance. My background includes a strong foundation in machine learning techniques such as risk modeling, telematics underwriting, and sentiment analysis using frameworks like XGBoost and PySpark. I am proficient in fine-tuning open-weight LLMs via LoRA and PEFT techniques and integrating human-in-the-loop frameworks to improve model precision and reliability. I enjoy collaborating cross-functionally to embed Generative AI capabilities into enterprise workflows, always striving to deliver adaptive, efficient, and context-aware AI solutions.…I am a Generative AI & AI/ML Engineer with over five years of experience building advanced agentic AI systems involving multi-agent workflows, tool-using LLMs, and stateful memory. I specialize in retrieval-augmented generation (RAG), semantic search, and orchestrating LLMs with LangChain, LangGraph, and OpenAI Agents SDK, focusing on creating reasoning agents with dynamic tool selection and automated decision-making. I have extensive experience deploying scalable AI systems on cloud platforms like Azure, AWS, and GCP, utilizing MLOps tools to maintain model governance and compliance. My background includes a strong foundation in machine learning techniques such as risk modeling, telematics underwriting, and sentiment analysis using frameworks like XGBoost and PySpark. I am proficient in fine-tuning open-weight LLMs via LoRA and PEFT techniques and integrating human-in-the-loop frameworks to improve model precision and reliability. I enjoy collaborating cross-functionally to embed Generative AI capabilities into enterprise workflows, always striving to deliver adaptive, efficient, and context-aware AI solutions.

Available to hire

I am a Generative AI & AI/ML Engineer with over five years of experience building advanced agentic AI systems involving multi-agent workflows, tool-using LLMs, and stateful memory. I specialize in retrieval-augmented generation (RAG), semantic search, and orchestrating LLMs with LangChain, LangGraph, and OpenAI Agents SDK, focusing on creating reasoning agents with dynamic tool selection and automated decision-making. I have extensive experience deploying scalable AI systems on cloud platforms like Azure, AWS, and GCP, utilizing MLOps tools to maintain model governance and compliance.

My background includes a strong foundation in machine learning techniques such as risk modeling, telematics underwriting, and sentiment analysis using frameworks like XGBoost and PySpark. I am proficient in fine-tuning open-weight LLMs via LoRA and PEFT techniques and integrating human-in-the-loop frameworks to improve model precision and reliability. I enjoy collaborating cross-functionally to embed Generative AI capabilities into enterprise workflows, always striving to deliver adaptive, efficient, and context-aware AI solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

AI/ML Engineer at CureMD

June 1, 2024 - Present

Designed and implemented agentic AI systems with multi-agent workflows, dynamic tool use, and long-term memory using LangChain, LangGraph, and OpenAI Agents SDK. Built retrieval-augmented generation pipelines integrating Pinecone vector databases and document transformers for context-aware responses. Developed reasoning agents utilizing function-calling LLMs and multi-step API orchestration for automating complex task decision-making. Engineered human-in-the-loop validation and citation-enforcing agents to enhance model reliability. Designed end-to-end ML pipelines using Azure Machine Learning and implemented MLOps workflows with CI/CD, MLflow, and Azure Purview. Fine-tuned open-weight LLMs like Mistral and LLaMA with LoRA and PEFT. Deployed scalable GenAI services using FastAPI, Docker, Kubernetes, and managed cross-cloud deployments on Azure. Integrated model routing and latency-accuracy mechanisms and applied experiment tracking with MLflow and Weights & Biases. Collaborated across

AI/ML Engineer at Western Alliance Bank

May 31, 2024 - August 29, 2025

Built and orchestrated Generative AI pipelines using OpenAI API, LLaMA-2, Mistral, and LangChain integrated with FAISS for RAG systems. Developed ML models using Scikit-learn, XGBoost, LightGBM, TensorFlow, and PyTorch for risk scoring, telematics underwriting, and sentiment analysis. Fine-tuned LLMs with LoRA, PEFT, and transfer learning techniques using Hugging Face Accelerate. Processed large-scale datasets for ML/NLP pipelines using pandas, NumPy, spaCy, NLTK, and OpenCV. Designed ML APIs with FastAPI and Flask, deploying microservices via Docker, Kubernetes, ONNX, TorchServe, and TorchScript. Managed AWS S3 cloud deployments and ensured model governance with MLflow, Weights & Biases, and Azure Purview. Automated CI/CD pipelines using GitHub Actions and Terraform and used PostgreSQL, MongoDB, SQLite, and Redis for data storage. Created visualizations with matplotlib, seaborn, Plotly, and Power BI. Collaborated in agile environments using Jupyter, VS Code, Google Colab, and Azure No

ML Engineer at Hexaware Technologies

July 1, 2022 - August 29, 2025

Developed and deployed ML models using scikit-learn, XGBoost, and LightGBM for customer segmentation, risk modeling, and churn prediction. Built data preprocessing pipelines with pandas, NumPy, spaCy, and NLTK for structured/unstructured data. Created RESTful prediction APIs using Flask and containerized services via Docker in Azure ML environments. Performed exploratory data analysis, feature engineering and reported insights using matplotlib, seaborn, and Plotly. Tracked experiments and models via MLflow, conducted cross-validation and A/B testing. Managed SQL and PostgreSQL data sources and used TF-IDF, embeddings, and topic modeling for NLP tasks. Collaborated with product and engineering teams to convert business problems to data-driven solutions. Deployed batch/real-time scoring jobs on Azure leveraging Blob Storage and Data Factory. Maintained reproducibility and code quality using Git and Jupyter Notebooks.