I am an AI/ML Engineer with four years of experience delivering production models, data pipelines, and GenAI features across regulated enterprises.
Experience
2025 — Now
2025 — Now
Riverwoods,Illinois,Usa
● Designed credit-risk feature pipelines in Python and SQL on AWS, improving training data consistency and enabling faster, auditable model iteration across governance checkpoints enterprisewide.
● Engineered PyTorch classification models with MLflow tracking, delivering explainable predictions through REST APIs and reducing manual underwriting review effort for frontline analysts daily consistently.
● Optimized SageMaker training jobs with GPU utilization and data sharding, shortening experimentation cycles and supporting timely regulatory model validation submissions with reproducible artifacts quarterly.
● Automated CI/CD workflows in GitHub and Docker, packaging inference services for Kubernetes deployment and improving release reliability across development, test, and production environments significantly.
● Validated model performance with A/B testing and drift monitoring, surfacing degradation early and protecting portfolio outcomes through controlled rollback, recalibration, and alerting processes proactively.
2025 — 2025
2025 — 2025
Nashville, TN
● Integrated clinical NLP pipelines with Transformers and Scikit-learn, extracting entities from notes and improving downstream analytics accuracy for care-operations stakeholders across facilities nationwide systemwide.
● Streamlined ETL pipelines in Airflow and Databricks, consolidating disparate datasets and enabling reproducible feature engineering for predictive readmission risk models at scale reliably consistently.
● Configured secure FastAPI services on Azure, exposing batch scoring endpoints and improving interoperability with hospital applications through documented API contracts and authentication controls end-to-end.
● Analyzed model fairness and bias metrics with Python, aligning thresholds with policy guidance and supporting transparent reporting for compliance and clinical leadership reviews routinely.
● Standardized MLOps runbooks with MLflow and Git, clarifying deployment steps and reducing on-call incidents during model releases across multiple teams and environments measurably operationally.
2022 — 2024
2022 — 2024
Hyderabad
● Implemented retrieval augmented generation solutions with LangChain and vector databases, improving knowledge access for support agents and reducing time-to-resolution on complex tickets substantially measurably.
● Orchestrated multi-agent workflows with LangGraph and CrewAI, coordinating tool calls and improving automation reliability for enterprise document processing tasks across shared services securely consistently.
● Refactored data services with Node.js and PostgreSQL, enhancing throughput for feature stores and enabling scalable training data access for ML pipelines reliably continuously.
● Deployed containerized workloads with Docker, Terraform, and AWS, provisioning repeatable environments and accelerating onboarding for new project teams, stakeholders, and delivery timelines quickly operationally.
● Monitored production inference with logging, observability, and model monitoring, tracing failures quickly and maintaining service SLAs for client-facing AI applications continuously at-scale reliably systemwide.
Education
The University of Texas at Arlington
Master of Science
SREE VENKATESWARA COLLEGE OF ENGINEERING