GenAI & MLOps Engineer for Enterprise AI

Jayashri YT

Email: **********@*****.*** Mobile: 575-***-****

AI/ML Engineer GenAI Engineer Data Scientist MLOps Machine Learning Predictive Analytics

Professional Summary:

Results-driven GenAI & AI/ML Engineer with 5+ years of experience designing, developing, and deploying enterprise-scale AI/ML and Generative AI solutions across Financial Services, Banking, and Software domains.

Expertise in Large Language Models (GPT-4, Claude, Gemini, Llama), Retrieval-Augmented Generation (RAG), AI Agents, Prompt Engineering, NLP, Deep Learning, Predictive Analytics, and MLOps.

Proven track record of building scalable AI platforms using Python, TensorFlow, PyTorch, LangChain, Hugging Face, AWS Bedrock, SageMaker, Spark, and Kubernetes that improve operational efficiency, automate business workflows, reduce manual effort, and accelerate enterprise digital transformation.

Experienced in deploying production-grade AI systems, model governance, cloud-native architectures, vector databases, CI/CD automation, and cross-functional collaboration to deliver secure, high-performance AI solutions.

Technical Skills:

Programming Languages: Python, SQL, Scala, Java, R

AI/ML: TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, CatBoost, OpenCV, ML Algorithms, Deep Learning

Generative AI: GPT-4, Claude, Gemini, Llama, Amazon Bedrock, OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, Prompt Engineering, Function Calling, AI Agents, RAG Pipelines

Natural Language Processing: spaCy, NLTK, Transformers, BERT, Sentiment Analysis, Semantic Search

Cloud Platforms: Amazon Web Services (AWS)

Big Data Technologies: Apache Spark, PySpark, Hadoop, Hive, Kafka, Airflow

MLOps & DevOps: MLflow, Kubeflow, Docker, Kubernetes, Jenkins, GitHub Actions, CI/CD, Model Registry, Model Monitoring

Databases: PostgreSQL, MySQL, Snowflake, MongoDB, Cassandra, DynamoDB

Visualization & BI: Power BI, Tableau, Plotly, Matplotlib, Seaborn

APIs & Frameworks: FastAPI, Django, Flask, REST APIs

Developer Tools: Git, GitHub, Jupyter Notebook, VS Code, PyCharm, Linux

Professional Experience:

Fulton Financial Corporation, Lancaster, PA Nov2024 – till date

GenAI & AI/ML Engineer

Key Responsibilities:

Designed and deployed enterprise-grade Generative AI applications using GPT-4, Claude, Gemini, and Llama, reducing manual financial operations by 50%.

Developed Retrieval-Augmented Generation (RAG) platforms using LangChain, OpenAI APIs, Hugging Face, vector databases, and semantic search, improving knowledge retrieval accuracy by 40%.

Engineered predictive analytics solutions for portfolio risk assessment and financial forecasting using ensemble learning techniques.

Automated enterprise ETL and ELT pipelines using AWS Glue, Airflow, Spark, and Lambda, reducing data processing time.

Designed conversational AI assistants supporting customer service, investment recommendations, and financial knowledge management.

Built AI-powered KYC, AML, and regulatory compliance solutions capable of processing thousands of financial documents daily while reducing review time by 65%.

Developed fraud detection and anomaly detection models using TensorFlow, PyTorch, XGBoost, and LightGBM, improving fraud detection recall by 30%.

Built scalable AI inference APIs using FastAPI, AWS Lambda and API Gateway supporting real-time applications.

Implemented enterprise MLOps pipelines using MLflow, Docker, Kubernetes, Jenkins, and GitHub Actions, reducing deployment cycles by 70%.

Optimized prompt engineering strategies, reducing LLM token usage while improving response quality and consistency.

Developed feature engineering pipelines utilizing Amazon Redshift, S3, Athena, and RDS for predictive analytics workloads.

Built Power BI and Plotly dashboards to monitor AI model performance, business KPIs, drift detection, and operational metrics.

Environment: GenAI, LangChain, OpenAI APIs, Python, TensorFlow, PyTorch, AWS, PySpark, Airflow, MLflow, Docker, Kubernetes, XGBoost, LightGBM

IBC Bank, Laredo, TX Jan2023 - Oct 2024

AI/ML Engineer

Key Responsibilities:

Developed enterprise AI and ML solutions for fraud detection, credit risk modeling, customer segmentation, and transaction analytics.

Designed scalable ML pipelines processing multi-terabyte banking datasets using Spark, PySpark, AWS EMR, and SageMaker.

Built secure Retrieval-Augmented Generation (RAG) applications enabling context-aware enterprise knowledge search.

Developed machine learning models for credit scoring, customer lifetime value prediction, and loan default forecasting.

Automated enterprise data ingestion pipelines using AWS Glue, Lambda, Step Functions, and Airflow, reducing manual intervention by 60%.

Deployed scalable ML models using Amazon SageMaker Real-Time and Batch Endpoints.

Implemented feature engineering and model optimization techniques, improving prediction accuracy by 20–30%.

Designed ETL frameworks integrating S3, Athena, Glue Catalog, Redshift, and EMR for enterprise banking analytics.

Performed advanced statistical analysis including regression, forecasting, hypothesis testing, and customer behavior analytics.

Developed executive dashboards using Power BI, Plotly, and Matplotlib to support data-driven business decisions.

Improved deployment efficiency through Git workflows, CI/CD pipelines, and MLflow model lifecycle management.

Environment: AI, ML, Python, PySpark, SageMaker, AWS, XGBoost, TensorFlow, Power BI, Spark, SQL, GenAI, MLflow

HighRadius Corporation, Houston, TX Apr2021 - Dec 2022

Data Scientist

Key Responsibilities:

Delivered AI solutions supporting customer analytics, marketing intelligence, forecasting, and enterprise automation initiatives.

Built recommendation systems, churn prediction models, and customer segmentation algorithms, improving marketing campaign effectiveness by 30%.

Developed predictive analytics solutions using Random Forest, Gradient Boosting, XGBoost, Logistic Regression, Clustering, and Neural Networks.

Built NLP applications using Amazon Bedrock, Hugging Face Transformers, SpaCy, and NLTK for sentiment analysis, document classification, and text analytics.

Designed end-to-end model deployment pipelines using AWS SageMaker, FastAPI, Flask, and REST APIs.

Implemented model monitoring using AWS CloudWatch, improving production reliability and governance.

Optimized feature engineering and hyperparameter tuning, improving model accuracy by 15–25%.

Collaborated with software engineering teams to integrate AI services into enterprise web applications.

Led technical design discussions, code reviews, mentoring sessions, and AI governance initiatives across engineering teams.

Environment: AI/ML, NLP, AWS SageMaker, Python, TensorFlow, PyTorch, Airflow, Kubeflow, FastAPI, Docker, Kubernetes

Education:

Bachelor of Technology in Information Technology (IT), Dr. Babasaheb Ambedkar Technological University, Maharashtra, India

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