Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I am an AI/ML Engineer with a passion for building production-ready AI solutions. I specialize in fine-tuning language models, building RAG pipelines, and deploying scalable ML systems that drive real business impact. I love turning complex data into intuitive, reliable products and collaborating with teams to ship high-quality AI features.…

Sai Sunai Madhamshetty

AI Engineer, Data Scientist, Data Analyst, +2





I am an AI/ML Engineer with a passion for building production-ready AI solutions. I specialize in fine-tuning language models, building RAG pipelines, and deploying scalable ML systems that drive real business impact. I love turning complex data into intuitive, reliable products and collaborating with teams to ship high-quality AI features.…

Available to hire

I am an AI/ML Engineer with a passion for building production-ready AI solutions. I specialize in fine-tuning language models, building RAG pipelines, and deploying scalable ML systems that drive real business impact. I love turning complex data into intuitive, reliable products and collaborating with teams to ship high-quality AI features.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI Engineer at Chime

March 1, 2024 - Present

Designed and deployed GenAI-powered recommendation systems using Transformers (BERT, GPT, T5) and reinforcement learning with human feedback (RLHF). Implemented RAG pipelines with LangChain and LlamaIndex, integrating vector databases (Pinecone & FAISS) for context-aware search and recommendations. Built scalable ML pipelines on Databricks and AWS SageMaker, orchestrated with Airflow, improving training/deployment cycle efficiency by 50%. Leveraged MLflow & W&B for experiment tracking and hyperparameter tuning; deployed models via Docker, Kubernetes, and FastAPI/Streamlit APIs to reduce latency by 30% and enable real-time scalability. Integrated AWS (S3, Redshift, Glue, Bedrock) for data storage and fine-tuned LLMs for financial insights, reducing fraud detection false positives by 20%. Set up model monitoring & drift detection pipelines to ensure reliability.

AI Engineer at Genpact

January 1, 2021 - December 1, 2022

Implemented ML models (Random Forest, SVMs, CNNs) and transitioned to LLM fine-tuning (GPT, LLaMA) for intelligent document processing and threat detection with 95% accuracy. Built microservices-based ML workflows using Flask, FastAPI, and AWS Lambda, reducing response time by 50%. Designed MLOps pipelines with Jenkins, Kubeflow, and GitHub Actions, automating CI/CD and reducing deployment cycles by 40%. Utilized Apache Kafka, Spark/PySpark, and Airflow for real-time streaming and batch processing of high-volume enterprise data. Integrated Azure Cognitive Services & GCP Vertex AI for NLP-driven automation, enhancing customer support efficiency by 30%. Built interactive Gradio/Streamlit dashboards for model explainability using SHAP and LIME, boosting stakeholder trust. Implemented data quality monitoring with dbt & Great Expectations, improving pipeline reliability by 35%.