Sathvik

AI/ML Engineer with 4+ years of experience building production-grade Machine Learning, Generative AI, Agentic AI, LLM, NLP, and Retrieval-Augmented Generation (RAG) solutions across financial services and enterprise domains. Expertise in AI agent workflows, multi-agent orchestration, customer analytics, credit risk modeling, fraud detection, semantic search, prompt engineering, LLM evaluation, and MLOps/LLMOps using Python, Pandas, PySpark, PyTorch, Google Cloud Platform (Vertex AI, BigQuery, Cloud Storage), AWS SageMaker, Docker, MLflow, FastAPI, LangChain, and LangGraph. Proven track record of delivering AI platforms that improve model performance, automate decision-making, and drive business outcomes.

AI/ML Engineer - State Street - USA

(2025-06)

• Built AI/ML pipelines using Python, Pandas, PySpark, XGBoost, LightGBM, Google Cloud Platform (Vertex AI, BigQuery), AWS SageMaker, enabling customer analytics, credit risk modeling, feature engineering, and deployment.
• Developed enterprise fraud detection solutions using PyTorch, Autoencoders, Isolation Forest, and Deep Learning models, processing over 500K daily financial transactions and improving fraud detection accuracy by 35%.
• Built Agentic AI and Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, AI Agents, Hugging Face Transformers, OpenAI GPT, Llama, BERT, Prompt Engineering, Tool Calling, FAISS, embeddings, semantic search, and LLM evaluation, supporting 200K+ monthly document queries and improving retrieval accuracy by 35%.
• Engineered scalable MLOps and LLMOps pipelines using MLflow, Docker, Git, Vertex AI Pipelines, SageMaker Pipelines, and CI/CD, automating model training, versioning, deployment, monitoring, and production governance.
• Designed scalable FastAPI inference services integrated with REST APIs and microservices, supporting more than 15K daily real-time and batch AI predictions with 99.9% service availability.
• Built distributed ETL pipelines using PySpark, SQL, Pandas, BigQuery, Cloud Storage, and enterprise data modeling, enhancing data quality, feature engineering, and AI model performance across large-scale customer and financial data.
• Collaborated with cross-functional teams to deploy AI solutions on Google Cloud Platform, implement responsible AI governance, monitor LLM performance, and deliver scalable solutions using Git-based Agile workflows.

Machine Learning Engineer - Mphasis - INDIA

(2019-05 - 2023-07)

• Built NLP document intelligence solutions using BERT, Hugging Face Transformers, Prompt Engineering, and LLM-based text classification, improving ticket routing, intent detection, and automation accuracy.
• Developed customer behavior prediction, customer segmentation, marketing analytics, and recommendation systems using Python, Pandas, Scikit-learn, Random Forest, XGBoost, and collaborative filtering, increasing recommendation CTR by 28% and customer engagement by 22%.
• Built scalable ETL pipelines using PySpark, SQL, Apache Spark, Pandas, and enterprise data modeling, supporting distributed data processing, feature engineering, and large-scale AI/ML workloads.
• Developed Agentic AI and semantic search platforms using LangChain, LangGraph, AI Agents, Hugging Face Transformers, Sentence Transformers, FAISS, RAG, Tool Calling, and autonomous workflows, indexing 1M+ documents and improving search relevance by 40%.
• Automated MLOps pipelines using Apache Airflow, MLflow, Python, PySpark, Google Cloud Platform (Vertex AI), AWS SageMaker, and Git-based CI/CD while deploying Machine Learning and Generative AI services with FastAPI, Docker, REST APIs, BigQuery, Cloud Storage, and microservices, supporting 500K+ monthly inference requests.

Masters - Information Systems - Syracuse University (2023-08 - 2025-05)