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Shreya Rajasekar

@shreyarajasekar

Gen AI Engineer at Ambry Genetics, developing Azure OpenAI agents and RAG workflows for code review, debugging, and remediation.

United States

Message

At Ambry Genetics, I design and deploy Azure OpenAI applications for code review, debugging, root-cause analysis, and remediation. I developed RAG pipelines using FAISS, Redis, and transformer embeddings, along with multi-agent workflows that support code understanding, issue diagnosis, fix generation, and validation.

At WaFd Bank, I built predictive risk models and deep learning systems, including a customer churn model that improved prediction accuracy by 21%. Earlier, I developed ML and data solutions at Columbia Bank and Cybage Software, and Python web applications and APIs at Ideabytes Software India.

Experience

Work history, roles, and key accomplishments

Current

GEN AI Engineer

Current

Ambry Genetics

Oct 2024 - Present (2 years)

Designed and deployed enterprise Generative AI solutions using Azure OpenAI, Python, LLMs, RAG, AI agents, and NLP to automate code review, debugging, root-cause analysis, and remediation workflows. Developed production-ready RAG pipelines and multi-agent AI workflows, and implemented MLOps and AI observability practices.

Azure OpenAI Python LLM RAG AI Agents NLP Faiss Redis Docker Kubernetes MLFlow Prometheus Grafana CI CD REST APIs MCP

AI/ML Engineer

WaFd Bank

Dec 2022 - Sep 2024 (1 year 9 months)

Built predictive risk models using Python, Scikit-learn, LightGBM, and XGBoost, and developed TensorFlow deep learning models for customer churn prediction. Implemented MLOps pipelines and monitoring solutions, and developed scalable ML services using FastAPI and GCP Vertex AI/BigQuery.

Python scikit learn LightGBM XGBoost TensorFlow DBSCAN Anomaly Detection LLM Evaluation Embeddings MLFlow Weights Biases Prometheus Grafana Cloudwatch fastAPI GCP BigQuery

ML Engineer

Columbia Bank

Apr 2020 - Nov 2022 (2 years 7 months)

Designed and implemented serverless data pipelines using AWS Lambda, Python, and Boto3, and built end-to-end ETL workflows. Developed machine learning models, deployed scalable solutions using Docker and Kubernetes, and managed MLOps pipelines using Jenkins, GitHub, and JIRA.

AWS Lambda Python AWS SDK (Boto3)ETL SQL Tableau Random Forest XGBoost Docker Kubernetes Jenkins GitHub Jira PySpark Hive AWS RedShift NLP

Data Scientist

Cybage Software

Sep 2018 - Dec 2019 (1 year 3 months)

Built predictive modeling systems for claim severity and risk profiles, and performed feature engineering and exploratory data analysis. Used Bayesian statistical approaches and created visual analytics with Plotly, while maintaining Grafana-based monitoring systems.

Python SQL PostgreSQL Bayesian Statistics Plotly Grafana EDA Feature Engineering

Python Developer

Ideabytes Software India

May 2017 - Aug 2018 (1 year 3 months)

Developed scalable web applications using Python and Django, built RESTful APIs, and automated cloud infrastructure provisioning with AWS Boto3. Created ETL scripts and managed version control with GitHub and JIRA.