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phanendra - Sr AI/ML engineer

[email protected]

Location: Bayonne, New Jersey, USA

Relocation: Yes

Visa: GC

Resume file: Phanindra_Guptaa_1786569881932.docx Please check the file(s) for viruses. Files are checked manually and then made available for download.

Phanindra Gupta Beechani Senior AI/ML Engineer | [email protected] PROFESSIONAL SUMMARY Senior AI Platform Engineer with 9+ years of experience designing, deploying, and supporting enterprise AI platforms and cloud-native applications across Azure and AWS environments. Expertise in building scalable Generative AI and LLM-based solutions using Azure OpenAI, OpenAI, Azure AI Foundry, LangChain, vector databases, and Retrieval-Augmented Generation (RAG) architectures. Strong experience implementing Infrastructure-as-Code using Terraform and Bicep, automating CI/CD pipelines with Azure DevOps and GitHub Actions, and deploying containerized AI workloads using Docker and Kubernetes. Skilled in developing secure AI platform reference architectures, integrating model gateways and vector databases, and enabling enterprise AI adoption through reusable cloud infrastructure. Experienced in Python development, FastAPI, REST APIs, AI governance, platform monitoring, and supporting regulated healthcare and enterprise environments with a focus on security, scalability, and operational excellence. TECHNICAL SKILLS LANGUAGES & DATA Python SQL R Pandas NumPy SciPy MACHINE LEARNING Scikit-learn XGBoost Random Forest Gradient Boosting Ensemble Learning Logistic Regression DEEP LEARNING / AI TensorFlow Keras Feedforward Neural Networks Isolation Forest Anomaly Detection CLOUD & MLOPS AWS (EC2, S3, IAM) Docker Apache Airflow FastAPI Flask CI/CD (Git). SageMaker BIG DATA & STREAMING Apache Spark (PySpark, Structured Streaming) Apache Kafka TOOLS & VISUALIZATION Tableau Power BI Matplotlib Seaborn ggplot2 Jupyter Git Anaconda PROFESSIONAL EXPERIENCE T-Mobile | Senior AI/ML Engineer Jan 2023 Present Designed and deployed enterprise-scale AI/ML and Generative AI pipelines using Python, Scikit-learn, XGBoost, TensorFlow, PyTorch, SQL, and AWS cloud services for customer personalization, churn prediction, and revenue optimization across 10M+ telecom subscriber records. Designed and implemented enterprise AI platform reference architectures supporting secure deployment of Generative AI and LLM-based applications across AWS and Azure cloud environments. Developed scalable AI solutions using Azure OpenAI, OpenAI APIs, LangChain, LangGraph, and Retrieval-Augmented Generation (RAG) for intelligent customer support, semantic search, and knowledge retrieval. Integrated vector databases including Pinecone and FAISS to build enterprise semantic search and contextual AI applications with optimized embedding retrieval. Built reusable AI platform services using FastAPI, Python, Docker, Kubernetes (EKS), and API Gateway to enable enterprise-wide AI application integration. Automated cloud infrastructure provisioning using Terraform and Infrastructure-as-Code principles for repeatable AI platform deployments. Developed CI/CD pipelines using GitHub Actions, Jenkins, and Azure DevOps for automated deployment, testing, and release management of AI services. Implemented model lifecycle management, monitoring, prompt versioning, logging, and operational observability using MLflow, CloudWatch, Grafana, and OpenSearch. Collaborated with Cloud Engineering, Security, DevOps, and Product teams to establish secure AI platform standards, governance, and enterprise deployment practices. Supported enterprise AI platform scalability through container orchestration, cloud-native microservices, and distributed inference architectures. Developed secure REST APIs and AI service integrations following enterprise authentication, RBAC, encryption, and API management standards. Environment: Python (Pandas, NumPy, Scikit-learn, XGBoost, SciPy, Statsmodels, Matplotlib, Seaborn), SQL (MySQL, SQL Server), Flask, R (caret, ggplot2), Azure OpenAI, Azure AI Foundry, OpenAI, LangChain, LangGraph, Pinecone, FAISS, FastAPI, Python, Docker, Kubernetes (EKS), Terraform, Azure DevOps, GitHub Actions, MLflow, AWS, API Gateway, SQL, Kafka, Databricks Health First | Data Scientist Jan 2021 Oct 2022 Built end-to-end ML pipelines using Python (Pandas, Scikit-learn) and SQL for patient risk stratification and hospital readmission forecasting processing HIPAA-compliant clinical datasets and delivering outputs directly into clinical decision workflows. Designed HIPAA-compliant AI platform components supporting secure deployment of Generative AI and predictive analytics applications for healthcare operations. Built Retrieval-Augmented Generation (RAG) solutions using LangChain, Azure OpenAI, vector databases, and semantic search for clinical document retrieval and knowledge management. Integrated Azure AI services with enterprise healthcare applications while maintaining HIPAA security and compliance requirements. Automated infrastructure provisioning using Terraform and CI/CD pipelines to standardize AI platform deployment across cloud environments. Developed Python-based AI services and REST APIs supporting clinical decision support and intelligent healthcare workflows. Implemented enterprise monitoring, logging, model versioning, and operational dashboards for AI platform reliability and governance. Collaborated with Security, Compliance, Cloud Engineering, and Data Engineering teams to establish secure AI deployment standards. Supported AI governance initiatives aligned with Responsible AI principles, NIST AI Risk Management Framework, and enterprise security policies. Optimized cloud-native AI infrastructure for high availability, scalability, and secure model serving. Participated in Agile delivery of enterprise AI platform capabilities supporting healthcare modernization initiatives. Performed exploratory data analysis using Pandas, Seaborn, and Matplotlib identifying missing data patterns, feature correlations, class imbalance, and distributional anomalies ahead of model development. Delivered advanced statistical reporting using R (caret, forecast, ggplot2) and Tableau dashboards consumed by clinical department heads and research teams to track model-driven patient risk insights. Environment: Python (Pandas, NumPy, Scikit-learn, SciPy, Statsmodels, Matplotlib, Seaborn), R (caret, forecast, ggplot2), SQL (MySQL, SQL Server), Tableau, Jupyter Notebook, Anaconda Lowe's | Data Analyst Feb 2019 Dec 2020 Extracted, transformed, and analysed large-scale retail datasets (~5M+ records) using advanced SQL window functions, complex joins, subqueries to surface operational insights informing inventory planning and regional sales strategy. Developed Python services and REST APIs enabling AI model integration into enterprise applications. Automated cloud resource provisioning using Terraform and Infrastructure-as-Code practices. Built CI/CD deployment pipelines using GitHub Actions supporting automated platform releases. Assisted with Docker containerization and Kubernetes deployment of analytics services. Implemented centralized monitoring, logging, and operational dashboards improving platform reliability. Supported enterprise data integration and cloud modernization initiatives across multiple business units. Collaborated with Platform Engineering and DevOps teams to improve scalability, automation, and operational efficiency. Environment: SQL (MySQL, SQL Server), Python (Pandas, NumPy, Matplotlib), R (ggplot2, dplyr), Power BI, Tableau, Advanced Excel (Pivot Tables, VBA), Jupyter Notebook, RStudio Earlier Career Synechron | Python Developer Aug 2014 Dec 2017 Designed and optimized Python-based data ingestion and transformation pipelines achieving a ~15% improvement in processing speed building scalable, automated data workflows for banking and financial services clients from the ground up. Built automated batch data processing workflows integrating SQL and Python scripts for scheduled data transformation, aggregation, and KPI report generation replacing error-prone manual reporting cycles across multiple client engagements. Extracted and analyzed large-scale structured datasets using advanced SQL (complex joins, aggregations, subqueries) to support operational reporting and business decision-making for banking clients. Developed and maintained Tableau and Power BI dashboards tracking client business KPIs and operational metrics presenting findings to business stakeholders in regular sprint review sessions. Performed exploratory data analysis using Python (Pandas, Matplotlib, Seaborn) to surface patterns, trends, and anomalies across client financial datasets, informing both tactical reporting and longer-term analytical strategy. Developed cloud-native Python services supporting enterprise platform modernization initiatives. Automated application deployment and infrastructure provisioning using Terraform and CI/CD pipelines. Built REST APIs and backend services supporting enterprise integrations and secure cloud deployments. Containerized Python applications using Docker and supported Kubernetes-based deployments. Assisted with implementation of API gateways and secure service communication across distributed environments. Participated in cloud migration initiatives, Infrastructure-as-Code implementation, and DevOps automation projects. Collaborated with Platform Engineering teams to improve application scalability, deployment consistency, and operational monitoring. Assisted in developing initial Scikit-learn classification models for transaction categorization tasks gaining foundational ML experience covering data preparation, feature engineering, model training, and evaluation. Collaborated with senior engineers and business analysts to translate client requirements into pipeline specifications; contributed to code reviews, technical documentation, and team coding standards. Maintained and refactored legacy Python scripts and SQL stored procedures improving readability, performance, and reliability across multiple active client data platforms simultaneously. Environment: Python (Pandas, NumPy, Matplotlib, Seaborn), SQL (MySQL, SQL Server), Tableau, Power BI, Scikit-learn, Jupyter Notebook, Git KEY PROJECTS AI-Powered Recommendation & Churn Prediction System T-Mobile Telecom US Problem: Generic plan recommendations drove low conversion; no predictive capability existed to identify at-risk subscribers before churn across T-Mobile's subscriber base. Built: End-to-end Python ML pipelines on ~10M+ records hybrid collaborative filtering recommendation engine combined with a Gradient Boosting churn classifier; Flask REST APIs for real-time inference; Tableau and Power BI dashboards for business visibility; automated ETL workflows for continuous model refresh. Outcome: ~18% uplift in recommendation performance; ~25% reduction in inference latency; improved subscriber retention tracking and churn risk visibility across business units. Stack: Python Scikit-learn XGBoost Gradient Boosting Flask Pandas NumPy SQL (MySQL) Tableau Power BI R (caret, ggplot2) Jupyter Notebook Clinical Risk Stratification & Predictive Analytics Platform Health First Healthcare US Problem: Clinical teams relied on manual retrospective reporting for patient risk assessment too slow and error-prone to support proactive care planning at scale. Built: HIPAA-compliant ML pipelines for patient risk stratification and readmission forecasting using Logistic Regression, Random Forest, and XGBoost classifiers; SQL-based ETL for clinical data extraction; Tableau dashboards for clinical stakeholder reporting. Outcome: ~20% improvement in model performance; reduced manual reporting effort; predictive insights adopted directly into clinical decision workflows by department leads. Stack: Python Scikit-learn XGBoost Logistic Regression Random Forest SQL (MySQL) R (caret, forecast, ggplot2) Tableau Pandas NumPy SciPy Statsmodels Retail Analytics & Reporting Automation Platform Lowe's Retail US Problem: Manual SQL reporting workflows created long turnaround cycles and inconsistent data quality, limiting the speed of operational decisions across merchandising and supply chain teams. Built: Automated SQL and Python pipelines replacing manual reporting; Power BI and Tableau dashboards tracking inventory turnover, regional sales, and supply chain KPIs; Excel VBA automation for cross-system data reconciliation. Outcome: ~30% reduction in manual reporting effort; faster insight delivery to senior leadership; standardized data pipeline patterns adopted across business units. Stack: SQL (MySQL, SQL Server) Python (Pandas, NumPy, Matplotlib) Power BI Tableau R (ggplot2, dplyr) Advanced Excel (VBA) Jupyter Notebook Banking Data Pipeline Automation & Analytics Synecron Banking & Consulting India Problem: Banking clients depended on slow, manual SQL reporting workflows with no standardized pipeline infrastructure for cross-system data reconciliation. Built: Python + SQL automated batch pipelines for data transformation and KPI delivery; Tableau and Power BI dashboards; initial Scikit-learn classification models for transaction categorization. Outcome: ~15% improvement in pipeline processing speed; eliminated manual reporting cycles; pipeline patterns reused across multiple client engagements. Stack: Python SQL (MySQL, SQL Server) Pandas NumPy Scikit-learn Tableau Power BI Matplotlib Jupyter Notebook Git Keywords: continuous integration continuous deployment artificial intelligence machine learning business intelligence sthree rlang