Hire Chandrakanth M. - Generative AI Engineer

* Built a Gen-AI chat bot prompt framework in Node.js using LangChain to generate role-aware GPT-4 prompts with 92% recall and precision monitored by Application Insights. * Automated model lifecycle in Azure ML with data-drift and performance monitors that trigger retraining pipelines without manual intervention. * Architected a secure microservices chat bot: React frontend with Chromatic testing, Node.js/Express RBAC backend enforcing Azure AD B2C JWTs, and multi-tier Delta Lake on ADLS Gen2 for logging and analytics. * Established end-to-end CI/CD via GitHub Actions and Terraform: linting, unit/integration tests, container builds, and blue/green deployments to Azure App Service and AKS. * Containerized GPT-4 API wrappers and embedding services with Docker and Helm in AKS, managed by KEDA HPA and Prometheus metrics to maintain p95 latency <120 ms. * Implemented observability using Prometheus, Grafana dashboards, and structured.

* Designed and ran HDInsight (HDFS/YARN/Hive) pipelines processing \~2 TB/day of HL7 messages and IoT telemetry, tuning HiveQL and Spark (partition pruning, broadcast joins) to boost nightly throughput by 35%. * Orchestrated 100+ daily Airflow v1.10 DAGs on Ubuntu VMs—using Jinja-templated configs and custom Python operators for REST ingestion and data validation—and configured email/SMS alerts for failures. * Developed a Django/Pandas monitoring portal with Plotly dashboards to visualize pipeline health and data-quality metrics, cutting incident-resolution time by 40%. * Migrated multi-step MapReduce/Hive workflows into streamlined PySpark jobs on HDInsight, leveraging in-memory compute for faster, more maintainable ETL. * Implemented early Azure Data Factory v1 pipelines to ingest on-prem Oracle and SQL Server data into Azure Data Lake Storage Gen1, extending the modern data platform. * Automated pytest unit and integration tests for critical ETL components within Azure DevOps CI/CD pipelines, catching 95% of regressions before production. * Enforced enterprise security by configuring Azure AD RBAC, storage-at-rest encryption, and Hive view row-level masking to safeguard PHI and meet regulatory standards. * Collaborated with data architects to define source-to-target mappings and refine CDC and late-arriving-data logic, ensuring all SLAs were met consistently. * Partnered with data analysts to translate business requirements into SQL-based data models and ad-hoc queries, accelerating insights delivery. * Built interactive Power BI and Excel dashboards for clinical and operational teams, enabling self-service analysis and reducing report requests by 50%. * Performed detailed data profiling and statistical analyses in Pandas to identify trends, outliers, and root causes of pipeline anomalies.