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AI/ML Engineer with 3+ years of experience building scalable machine learning and Generative AI solutions across banking, fintech, and digital platforms. Experienced in developing RAG pipelines, LLM fine-tuning (LoRA), prompt engineering, and vector search systems using Azure OpenAI, LangChain, Pinecone, and Azure Cognitive Search. Strong expertise in fraud detection, credit risk modeling, NLP, and predictive analytics using Python, PySpark, XGBoost, LightGBM, BERT, and SQL, with hands-on experience in MLOps, MLflow, Databricks, SageMaker, Docker, Kubernetes, Airflow, CI/CD, and Terraform to deploy secure, scalable, and production-ready AI systems.…AI/ML Engineer with 3+ years of experience building scalable machine learning and Generative AI solutions across banking, fintech, and digital platforms. Experienced in developing RAG pipelines, LLM fine-tuning (LoRA), prompt engineering, and vector search systems using Azure OpenAI, LangChain, Pinecone, and Azure Cognitive Search. Strong expertise in fraud detection, credit risk modeling, NLP, and predictive analytics using Python, PySpark, XGBoost, LightGBM, BERT, and SQL, with hands-on experience in MLOps, MLflow, Databricks, SageMaker, Docker, Kubernetes, Airflow, CI/CD, and Terraform to deploy secure, scalable, and production-ready AI systems.

Subrahmanyam Patamsetti

Data Scientist, AI Engineer, AI Developer, +2

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AI/ML Engineer with 3+ years of experience building scalable machine learning and Generative AI solutions across banking, fintech, and digital platforms. Experienced in developing RAG pipelines, LLM fine-tuning (LoRA), prompt engineering, and vector search systems using Azure OpenAI, LangChain, Pinecone, and Azure Cognitive Search. Strong expertise in fraud detection, credit risk modeling, NLP, and predictive analytics using Python, PySpark, XGBoost, LightGBM, BERT, and SQL, with hands-on experience in MLOps, MLflow, Databricks, SageMaker, Docker, Kubernetes, Airflow, CI/CD, and Terraform to deploy secure, scalable, and production-ready AI systems.…AI/ML Engineer with 3+ years of experience building scalable machine learning and Generative AI solutions across banking, fintech, and digital platforms. Experienced in developing RAG pipelines, LLM fine-tuning (LoRA), prompt engineering, and vector search systems using Azure OpenAI, LangChain, Pinecone, and Azure Cognitive Search. Strong expertise in fraud detection, credit risk modeling, NLP, and predictive analytics using Python, PySpark, XGBoost, LightGBM, BERT, and SQL, with hands-on experience in MLOps, MLflow, Databricks, SageMaker, Docker, Kubernetes, Airflow, CI/CD, and Terraform to deploy secure, scalable, and production-ready AI systems.

Available to hire

AI/ML Engineer with 3+ years of experience building scalable machine learning and Generative AI solutions across banking, fintech, and digital
platforms. Experienced in developing RAG pipelines, LLM fine-tuning (LoRA), prompt engineering, and vector search systems using Azure OpenAI,
LangChain, Pinecone, and Azure Cognitive Search. Strong expertise in fraud detection, credit risk modeling, NLP, and predictive analytics using Python,
PySpark, XGBoost, LightGBM, BERT, and SQL, with hands-on experience in MLOps, MLflow, Databricks, SageMaker, Docker, Kubernetes, Airflow, CI/CD,
and Terraform to deploy secure, scalable, and production-ready AI systems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at PNC Bank, USA

June 1, 2025 - Present

Architected enterprise-grade Retrieval-Augmented Generation (RAG) solution using Azure OpenAI, LangChain, and Pinecone to enable contextual intelligence over regulatory and policy documents, reducing compliance query resolution time. Engineered LLM-driven credit risk summarization pipelines using GPT-4 and prompt orchestration strategies, automating underwriting report generation and improving analyst productivity. Deployed containerized transformer models with FastAPI, Docker, and Kubernetes for scalable real-time inference with sub-150ms latency. Implemented model monitoring and governance using MLflow and Azure AI safety controls to detect drift and hallucinations. Designed distributed Databricks and PySpark data pipelines to transform structured and unstructured financial data into ML-ready features, accelerating fraud model retraining cycles. Automated CI/CD workflows using GitHub Actions and Terraform to streamline model versioning and infrastructure provisioning.

Machine Learning Engineer at Phonepe, India

April 1, 2022 - August 1, 2023

Developed real-time fraud detection models using XGBoost and LightGBM to analyze UPI transaction streams, reducing fraudulent transactions by 23%. Constructed feature engineering pipelines processing 50M+ daily transactions to enhance model precision by 17%. Deployed ML models using Docker and AWS SageMaker for scalable batch and real-time inference workflows, to maintain 99.9% production uptime. Designed customer segmentation models using K-Means and behavioral analytics to personalize cashback and engagement campaigns, boosting campaign conversions by 14%. Implemented A/B testing frameworks and SHAP explainability to support RBI compliance, and streamlined data ingestion with Airflow and AWS S3 to automate retraining and reduce manual intervention by 30%. Collaborated on NLP-based intent classification using BERT embeddings to improve chatbot accuracy.

Data Analyst at Adobe, India

January 1, 2021 - March 1, 2022

Built interactive dashboards using Tableau and SQL to monitor Adobe Experience Cloud campaign performance; analyzed user behavioral datasets to identify churn predictors; designed ETL workflows to centralize multi-channel campaign data; conducted cohort and funnel analyses to inform UX optimization; automated KPI reporting pipelines; assisted in predictive modeling experiments using logistic regression to forecast subscription renewals.