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Senior Generative AI Engineer with 10+ years of experience building scalable machine learning and enterprise AI solutions using Python, cloud platforms, and production-grade MLOps. Skilled in designing predictive and NLP systems, as well as building Generative AI applications using LLMs, RAG, and agentic workflows for regulated and high-impact domains. Focused on delivering reliable, secure, and measurable AI outcomes—partnering with cross-functional teams to deploy model pipelines, build backend services, and create monitoring/guardrails for production use. Experienced with Kubernetes/Docker, FastAPI microservices, vector databases, and end-to-end automation that improves efficiency and decision quality across finance, insurance, and healthcare.…Senior Generative AI Engineer with 10+ years of experience building scalable machine learning and enterprise AI solutions using Python, cloud platforms, and production-grade MLOps. Skilled in designing predictive and NLP systems, as well as building Generative AI applications using LLMs, RAG, and agentic workflows for regulated and high-impact domains. Focused on delivering reliable, secure, and measurable AI outcomes—partnering with cross-functional teams to deploy model pipelines, build backend services, and create monitoring/guardrails for production use. Experienced with Kubernetes/Docker, FastAPI microservices, vector databases, and end-to-end automation that improves efficiency and decision quality across finance, insurance, and healthcare.

Samhitha Medulla

AI Engineer, Data Scientist, Full Stack Developer, +2

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Senior Generative AI Engineer with 10+ years of experience building scalable machine learning and enterprise AI solutions using Python, cloud platforms, and production-grade MLOps. Skilled in designing predictive and NLP systems, as well as building Generative AI applications using LLMs, RAG, and agentic workflows for regulated and high-impact domains. Focused on delivering reliable, secure, and measurable AI outcomes—partnering with cross-functional teams to deploy model pipelines, build backend services, and create monitoring/guardrails for production use. Experienced with Kubernetes/Docker, FastAPI microservices, vector databases, and end-to-end automation that improves efficiency and decision quality across finance, insurance, and healthcare.…Senior Generative AI Engineer with 10+ years of experience building scalable machine learning and enterprise AI solutions using Python, cloud platforms, and production-grade MLOps. Skilled in designing predictive and NLP systems, as well as building Generative AI applications using LLMs, RAG, and agentic workflows for regulated and high-impact domains. Focused on delivering reliable, secure, and measurable AI outcomes—partnering with cross-functional teams to deploy model pipelines, build backend services, and create monitoring/guardrails for production use. Experienced with Kubernetes/Docker, FastAPI microservices, vector databases, and end-to-end automation that improves efficiency and decision quality across finance, insurance, and healthcare.

Available to hire

Senior Generative AI Engineer with 10+ years of experience building scalable machine learning and enterprise AI solutions using Python, cloud platforms, and production-grade MLOps. Skilled in designing predictive and NLP systems, as well as building Generative AI applications using LLMs, RAG, and agentic workflows for regulated and high-impact domains.

Focused on delivering reliable, secure, and measurable AI outcomes—partnering with cross-functional teams to deploy model pipelines, build backend services, and create monitoring/guardrails for production use. Experienced with Kubernetes/Docker, FastAPI microservices, vector databases, and end-to-end automation that improves efficiency and decision quality across finance, insurance, and healthcare.

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Work Experience

Generative AI Engineer at HSBC

January 1, 2024 - Present

Built a production-grade multi-agent AI credit decisioning and underwriting automation platform. Coordinated multiple specialized agents (intake, document intelligence, verification, fraud detection, credit risk, policy compliance, decisioning, and explainability) using LangGraph/LangChain. Implemented RAG pipelines with semantic retrieval and vector search (Pinecone/FAISS/OpenSearch), integrated enterprise data sources and credit bureau APIs, and deployed event-driven workflows using Kafka/Airflow/Lambda for document ingestion and agent execution. Developed FastAPI/Python microservices and secured integrations for high-volume lending workflows, including WebSocket streaming and human-in-the-loop review with audit traceability. Deployed containerized services on Amazon EKS with ~99.5% availability, and implemented MLOps/monitoring via MLflow, CloudWatch/Prometheus/Grafana/Langfuse with IAM/Secrets Manager security controls. Improved processing consistency and reduced manual review effo

Data Scientist – NLP Focus at GEICO Insurance

June 1, 2022 - December 31, 2023

Developed an insurance claims fraud detection and risk intelligence platform. Built end-to-end ML pipelines for ingesting and transforming claims data using Python/SQL/PySpark and Azure Data Factory/ADLS. Implemented data quality validation workflows, engineered fraud indicators, and applied NLP analytics using spaCy and Hugging Face/ Azure AI Language to analyze adjuster notes and claim documents. Trained classification models (Logistic Regression, Random Forest, Gradient Boosting, XGBoost) and improved evaluation with metrics such as precision/recall/ROC-AUC, reducing false positives. Integrated Azure ML for training, registry, monitoring (Model Monitor/App Insights), retraining automation, and governance. Exposed scoring via FastAPI REST APIs, created stakeholder dashboards in Power BI/Synapse, and implemented CI/CD with Azure DevOps/Docker to reduce release cycle time. Added explainability using SHAP and automated investigation prioritization based on risk scores.

Data Scientist at State of NM

September 1, 2019 - May 31, 2022

Built healthcare predictive analytics and decision support capabilities for statewide public health operations. Developed demand/resource forecasting models and ML pipelines using Python, SQL, PySpark, and orchestration via Apache Airflow. Created standardized analytical features (Feast), implemented scalable data transformations and anomaly detection, and delivered insights through MLflow-tracked training/evaluation workflows. Built secure FastAPI-based backend services to serve predictions, deployed containerized workloads on AWS EKS, and supported data processing with S3/Glue/Lambda/CloudWatch. Delivered reporting/visualizations in Tableau/Power BI and automated CI/CD via GitHub Actions. Implemented model monitoring and reliability tracking for production analytics environments.

Python Full Stack Developer at Truist Financial

September 1, 2017 - August 31, 2019

Developed an embedded banking API and financial data integration platform for transaction processing, reconciliation, and operational reporting. Implemented Python/Django-based backend services and REST APIs for banking system integrations, authentication/authorization (OAuth/JWT/Azure AD), and optimized SQL queries to reduce latency for frequently accessed operations. Built reconciliation/validation workflows to improve transaction accuracy and reduced manual review. Added operational monitoring using Azure Monitor/logging, improved throughput via async processing, and created dashboards in Tableau/Power BI for business stakeholders.

Python Developer at PayPal

August 1, 2013 - August 31, 2016

Built a payment integration and transaction intelligence platform to automate transaction monitoring, reconciliation, and reporting. Developed Python/Django/Flask REST APIs and ETL/data processing pipelines using Pandas/NumPy/SQL, improving reporting accuracy and reducing data retrieval times. Implemented AWS-based workflows using S3/Lambda/RDS and added monitoring with CloudWatch. Designed validation/anomaly detection logic for irregular payment patterns. Supported secure integrations using authentication frameworks, improved processing throughput via backend optimizations, and delivered operational analytics dashboards in Tableau/Matplotlib/Excel.