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rohith reddy kandakatla - senior Full Stack AI Developer

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Rohith Reddy Kandakatla Senior Full Stack AI Developer +1(919) -404 7451|[email protected]|Linkedin PROFESSIONAL SUMMARY: Experienced Senior AI Full Stack Developer specializing in enterprise artificial intelligence solutions, progressing from Python development and platform engineering into intelligent applications leveraging Generative AI,modern cloud architectures. Architected enterprise AI applications utilizing Azure OpenAI for intelligent automation, enabling contextual reasoning through GPT-4o while supporting scalable business workflows using LangChain frameworks. Built intelligent orchestration platforms utilizing LangGraph for workflow coordination, enabling autonomous task execution through CrewAI while supporting enterprise automation using Agentic AI capabilities. Developed advanced retrieval ecosystems utilizing RAG for contextual grounding, enabling enterprise knowledge discovery through GraphRAG while supporting semantic information access using Azure AI Search platforms. Implemented enterprise observability frameworks utilizing LangSmith for response evaluation, enabling quality validation through Ragas while supporting continuous optimization using Arize Phoenix monitoring platforms. Designed scalable AI deployment architectures utilizing AKS for container orchestration, enabling resilient inference services through LLM Serving while supporting enterprise reliability using Docker environments. Developed cloud-native machine learning platforms utilizing AWS SageMaker for model lifecycle management, enabling governance through MLflow while supporting scalable operations using Kubernetes infrastructure. Engineered predictive intelligence solutions utilizing Scikit-Learn for analytical modeling, enabling forecasting through XGBoost while supporting enterprise decision systems using TensorFlow frameworks. Implemented reusable data pipelines utilizing PySpark for distributed processing, enabling governed feature management through SageMaker Feature Store while supporting workflow automation using Apache Airflowplatforms. Delivered secure backend applications utilizing Python for service development, enabling scalable business automation through FastAPI while supporting distributed integrations using Django frameworks. Built responsive enterprise applications utilizing React for user experiences, enabling workflow modernization through Angular while supporting scalable frontend development using TypeScript technologies. Engineered large-scale analytics platforms utilizing Databricks for distributed processing, transforming enterprise datasets through Apache Spark while supporting real-time integrations using Kafka ecosystems. Worked extensively across Microsoft Azure and Amazon Web Services environments delivering enterprise AI solutions, while gaining exposure to Google Vertex AI through evaluation, experimentation, and prototyping initiatives. Developed enterprise operational platforms utilizing Oracle for information management, enabling regulatory automation through SAP ERP while supporting manufacturing integrations using MES/LIMS environments. Technical Skills: Category Technologies Programming & Scripting Python, SQL, PySpark, Pandas, NumPy, TypeScript, JavaScript, Shell Scripting Generative AI & Agentic AI Azure OpenAI, GPT-4o, Claude 3, Llama 3, LangChain, LangGraph, CrewAI, AutoGen, Hugging Face Transformers, Sentence Transformers, Semantic Kernel, MCP Protocol, Function Calling, Tool Calling, Prompt Engineering, Context Engineering, ReAct, RAG, GraphRAG, Multi-Agent Systems Machine Learning & MLOps Scikit-Learn, TensorFlow, PyTorch, XGBoost, Random Forest, Logistic Regression, K-Means Clustering, Time Series Forecasting, Feature Engineering, Hyperparameter Tuning, MLflow, SageMaker Feature Store, Model Registry, Model Monitoring Cloud Platforms & Services Microsoft Azure (Azure OpenAI, AKS, Azure AI Search, Azure Data Factory, Azure Data Lake Storage Gen2, Azure SQL Database, Cosmos DB, Event Hubs, Azure App Services, Azure DevOps, Azure Key Vault), Amazon Web Services (SageMaker, EKS, S3, Redshift, Lambda, IAM, ECR, VPC, CloudWatch) Data Engineering & Big Data Databricks, Apache Spark, Spark SQL, Hadoop, Hive, Kafka, Airflow, ETL/ELT Pipelines, Data Lake Architecture, Data Warehousing, Star Schema, Dimensional Modeling, Data Validation, Anomaly Detection Databases & Vector Stores Oracle, PostgreSQL, SQL Server, Azure SQL Database, Cosmos DB, Amazon Redshift, Pinecone, pgvector Backend & API Development FastAPI, Django, Flask, REST APIs, Microservices Architecture, Event-Driven Architecture, JSON, XML, Django ORM Frontend Technologies React.js, Angular, TypeScript, JavaScript, HTML5, CSS3 DevOps, Containers & Observability Docker, Containerization, Kubernetes (AKS/EKS), CI/CD Pipelines, GitHub Actions, Jenkins, Terraform, Prometheus, Grafana, Azure Monitor, Arize Phoenix, LangSmith, Ragas, TruLens Security, Testing & Collaboration OAuth 2.0, JWT, RBAC, Azure Active Directory, PyTest, Integration Testing, UAT, Swagger, Confluence, JIRA, Git, Agile Scrum, Linux Professional Experience: Blue Cross Blue Shield (BCBS)| Durham, North Carolina Jan 2024 Present Role: Senior AI Developer Responsibilities Architected an enterprise healthcare Generative AI platform using Azure OpenAI, FastAPI, and Python, enabling intelligent knowledge retrieval, clinical workflow automation, and secure decision-support capabilities across organizations. Designed cloud-native architecture using Microservices Architecture, Event-Driven Architecture,Agentic AI, enabling scalable autonomous workflows, distributed processing, resilient integrations,enterprise healthcare system interoperability requirements. Collaborated within Agile delivery teams using Agile Scrum, JIRA, and Confluence, participating in sprint ceremonies, backlog refinement, stakeholder discussions, and iterative healthcare platform enhancements regularly. Built enterprise data ingestion pipelines using FHIR APIs, Azure Data Lake Storage, and Event Hubs, enabling reliable acquisition of healthcare claims, provider, member, and clinical datasets. Developed large-scale data processing workflows using Databricks, PySpark, and Azure Data Factory, transforming structured and unstructured healthcare information into analytics-ready datasets supporting downstream AI applications. Designed scalable persistence layers using Cosmos DB, PostgreSQL, and Azure SQL, enabling secure storage, rapid retrieval, and governance of healthcare knowledge assets enterprise-wide. Implemented vector retrieval infrastructure using Pinecone, Azure AI Search, and pgvector, enabling semantic embeddings, contextual retrieval, and knowledge grounding for Generative AI applications. Evaluated foundation models including GPT-4o, Claude 3, and Llama 3, selecting optimal architectures based on reasoning quality, contextual accuracy, latency requirements, and healthcare domain performance benchmarks. Developed enterprise retrieval workflows using RAG, GraphRAG, and Semantic Search, enabling grounded responses, multi-hop reasoning, contextual understanding, and accurate healthcare knowledge discovery across datasets. Optimized Generative AI performance through Prompt Engineering, Context Engineering, and ReAct, improving response relevance, reasoning consistency, retrieval effectiveness, and structured output generation reliability. Built Agentic AI workflows using LangChain, LangGraph, and CrewAI, enabling multi-agent collaboration, workflow orchestration, autonomous task execution, and intelligent healthcare process automation capabilities. Developed semantic embedding frameworks using Hugging Face, Sentence Transformers, and Embeddings, generating contextual vector representations supporting enterprise search, retrieval accuracy, and knowledge grounding initiatives. Implemented evaluation frameworks using LangSmith, Ragas, and TruLens, enabling hallucination detection, response scoring, quality assessment, and continuous improvement across production AI systems. Containerized enterprise AI services using Docker, Azure Container Registry, and AI Microservices, enabling portable deployments, service isolation, scalability, and consistent execution across multiple environments. Orchestrated production workloads using Azure Kubernetes Service, LLM Serving, and Model Serving, enabling scalable inference, automated resource management, high availability, and enterprise AI operations. Developed automated deployment pipelines using Azure DevOps, GitHub Actions, and CI/CD, enabling continuous integration, streamlined releases, version control, and reliable software delivery processes. Managed cloud infrastructure using Terraform, Azure Key Vault, and IAM, enabling secure resource provisioning, secrets management, governance controls, and compliant enterprise cloud operations. Developed observability frameworks using Arize Phoenix, Prometheus, and Azure Monitor, enabling performance monitoring, hallucination detection, latency tracking, reliability measurement, and proactive issue identification across production. Built comprehensive quality assurance processes using PyTest, Integration Testing, and API Validation, ensuring application stability, functional accuracy, deployment readiness, and consistent enterprise system behavior. Authored technical documentation using Swagger, Confluence, and Architecture Diagrams, supporting knowledge transfer, onboarding efficiency, API governance, audit readiness, and cross-functional collaboration initiatives. Environment: Python, FastAPI, Django, React.js, TypeScript, REST APIs, Microservices Architecture, Event-Driven Architecture, Generative AI, Agentic AI, Azure OpenAI, GPT-4o, Claude 3, Llama 3, LangChain, LangGraph, CrewAI, AutoGen, Hugging Face Transformers, Sentence Transformers, Semantic Kernel, Multi-Agent Systems, MCP Protocol, Function Calling, Tool Calling, Prompt Engineering, Context Engineering, ReAct, RAG, GraphRAG, Semantic Search, Semantic Embeddings, Vector Databases, Pinecone, Azure AI Search, pgvector, Knowledge Graphs, FHIR APIs, Azure Data Lake Storage Gen2, Azure Event Hubs, Azure Data Factory, Databricks, PySpark, Cosmos DB, PostgreSQL, Azure SQL Database, Docker, Azure Container Registry (ACR), Azure Kubernetes Service (AKS), LLM Serving, Model Serving, AI Microservices, Azure Functions, Azure DevOps, GitHub Actions, CI/CD Pipelines, Terraform, Azure Key Vault, IAM, LangSmith, Ragas, TruLens, Arize Phoenix, Azure Monitor, Prometheus, PyTest, Integration Testing, API Validation, Swagger, Confluence, Agile Scrum, JIRA, Linux. Pennymac | Westlake Village, CA Jul 2021 Dec 2023 Role: AI / ML Engineer Responsibilities Developed enterprise mortgage risk analytics platform using AWS SageMaker, Python, and MLflow, enabling scalable model deployment, automated risk assessment, and data-driven decision-making across lending and servicing operations. Designed cloud-native machine learning architecture using AWS SageMaker, Kubernetes, and Microservices Architecture, enabling automated training workflows, scalable inference services, lifecycle governance, resilient enterprise model deployment. Collaborated within Agile delivery teams using Agile Scrum, JIRA, and Confluence, participating in sprint planning, stakeholder reviews, backlog refinement, and iterative machine learning solution development initiatives. Built enterprise data ingestion pipelines integrating Amazon S3, PostgreSQL, and REST APIs, enabling seamless acquisition of mortgage, borrower, servicing, and financial datasets across lending platforms. Developed large-scale data processing workflows using PySpark, Pandas, and Apache Airflow, transforming raw mortgage datasets into structured formats supporting machine learning and analytics initiatives. Designed scalable storage architecture using Amazon S3, PostgreSQL, and AWS Redshift, enabling centralized data management, analytical processing, and efficient access across enterprise environments. Implemented feature management frameworks using SageMaker Feature Store, Feature Engineering, and Data Quality Validation, enabling reusable training datasets, model consistency, and reliable predictive analytics outcomes. Developed predictive risk models using XGBoost, Scikit-Learn, and TensorFlow, supporting borrower risk scoring, delinquency prediction, credit analytics, and mortgage portfolio performance forecasting initiatives. Built forecasting workflows using Time Series Analysis, Pandas, and Statsmodels, enabling trend analysis, portfolio monitoring, repayment behavior prediction, and financial planning across lending operations. Optimized model performance using Hyperparameter Tuning, Cross-Validation, and Feature Selection, improving prediction stability, training efficiency, generalization quality, and explainability across mortgage analytics models. Managed experimentation lifecycle using MLflow Tracking, Model Registry, and SageMaker Experiments, enabling reproducibility, version control, metric comparison, and governed promotion of production-ready models. Applied engineering standards using Python OOP, Modular Architecture, and Design Patterns, ensuring reusable components, maintainable pipelines, reduced complexity, and scalable machine learning codebases. Validated model readiness using A/B Testing, Statistical Analysis, and Offline Evaluation, ensuring business alignment, model robustness, measurable lift, and reliable production decision-support outcomes. Containerized machine learning applications using Docker, Amazon ECR, and Microservices Architecture, enabling portable deployments, environment consistency, scalable execution, and streamlined model delivery across environments. Orchestrated production machine learning workloads using Amazon EKS, SageMaker Pipelines, and Kubernetes, enabling automated training, deployment scheduling, resource optimization, and scalable model lifecycle management. Developed automated deployment workflows using Jenkins, GitHub Actions, and AWS CodePipeline, enabling continuous integration, release automation, version control, and reliable machine learning software delivery. Managed cloud infrastructure using Terraform, AWS IAM, and VPC Networking, enabling secure resource provisioning, governance enforcement, network isolation, and compliant enterprise cloud operations Developed model monitoring frameworks using CloudWatch, Prometheus, and Grafana, enabling performance tracking, drift detection, operational visibility, proactive alerting, and continuous oversight of production models. Built comprehensive testing strategies using PyTest, Integration Testing, and User Acceptance Testing, ensuring model reliability, deployment stability, functional accuracy, and consistent business outcomes. Authored technical documentation using Swagger, Confluence, and Architecture Diagrams, supporting regulatory compliance, audit readiness, knowledge transfer, API governance, and cross-functional engineering collaboration initiatives. Environment: Python, SQL, Pandas, NumPy, PySpark, Apache Airflow, AWS SageMaker, SageMaker Pipelines, SageMaker Feature Store, MLflow, Scikit-Learn, XGBoost, TensorFlow, PyTorch, Random Forest, Logistic Regression, Time Series Analysis, Statsmodels, Feature Engineering, Hyperparameter Tuning, Cross-Validation, Statistical Analysis, Predictive Modeling, Risk Analytics, Forecasting, Model Registry, Model Monitoring, Drift Detection, Docker, Amazon ECR, Kubernetes (EKS), Microservices Architecture, AWS Lambda, Amazon S3, PostgreSQL, Amazon Redshift, REST APIs, Jenkins, GitHub Actions, AWS CodePipeline, CI/CD Pipelines, Terraform, AWS IAM, VPC Networking, CloudWatch, Prometheus, Grafana, PyTest, Integration Testing, User Acceptance Testing (UAT), Swagger, Confluence, Azure OpenAI, GPT-3.5, Prompt Engineering, Document Summarization, Semantic Search, Agile Scrum, JIRA, Linux. State of Pennsylvania | Harrisburg, PA Dec 2019 Jun 2021 Role: Senior Full Stack Developer Responsibilities Developed enterprise citizen services platform using Python, Django, and React, delivering scalable digital applications supporting citizens, agencies, government stakeholders, and public service modernization initiatives statewide. Designed distributed application architecture using Microservices Architecture, REST APIs, and Event-Driven Architecture, enabling scalable citizen portals, workflow automation, system interoperability, and efficient enterprise service delivery. Collaborated within Agile delivery teams using Agile Scrum, JIRA, and Confluence, participating in sprint planning, stakeholder reviews, backlog refinement, and iterative application development initiatives. Built enterprise integration pipelines using Oracle, PostgreSQL, and REST APIs, enabling secure data exchange, automated workflows, and seamless connectivity across legacy systems and departmental platforms. Developed data processing solutions using Python, Pandas, and SQL, transforming case management information while optimizing databases through indexing, stored procedures, and query performance improvements. Implemented governance frameworks using Audit Logging, Validation Rules, and Security Controls, ensuring regulatory compliance, data integrity, operational transparency, and reliable public-sector information management processes. Developed responsive citizen-facing applications using React, Angular, and TypeScript, delivering accessible user experiences, reusable components, scalable interfaces, and modern digital government services statewide. Built reusable interface frameworks using JavaScript, HTML5, and CSS3, improving maintainability, responsiveness, consistency, and cross-browser compatibility across enterprise web applications and citizen portals. Developed secure backend services using FastAPI, Django REST Framework, and REST APIs, enabling business process automation, scalable integrations, and distributed government application development. Implemented identity management solutions using OAuth 2.0, JWT, and RBAC, enabling secure authentication, role-based authorization, regulatory compliance, and controlled access across citizen service applications. Optimized application performance using Caching, Asynchronous Processing, and Design Patterns, improving scalability, response times, maintainability, and overall user experience across enterprise government platforms. Containerized enterprise applications using Docker, Azure App Services, and Azure SQL Database, enabling cloud modernization, scalable deployments, secure hosting, and improved operational efficiency. Developed automated deployment workflows using Azure DevOps, Git, and CI/CD Pipelines, enabling continuous integration, release consistency, production support, monitoring, and reliable software delivery practices. Developed quality assurance processes using PyTest, Integration Testing, and User Acceptance Testing, ensuring application reliability, deployment readiness, functional accuracy, and consistent business outcomes. Authored technical documentation using Confluence, API Documentation, and System Design Specifications, supporting knowledge transfer, regulatory compliance, onboarding efficiency, and cross-functional engineering collaboration initiatives. Environment: Python, Django, FastAPI, React.js, Angular, TypeScript, REST APIs, Microservices Architecture, Oracle, PostgreSQL, SQL, OAuth 2.0, JWT, RBAC, Docker, Azure App Services, Azure SQL Database, Azure Active Directory, Azure DevOps, Git, CI/CD, PyTest, Agile Scrum, JIRA, Linux. Costco Wholesale | Issaquah, WA. Oct 2017 Nov 2019 Role: Data Science Engineer Responsibilities Developed enterprise retail analytics platform using Python, PySpark, and Databricks, enabling inventory forecasting, customer segmentation, merchandising optimization, and data-driven decision-making across retail operations enterprise-wide. Designed scalable data lake architecture using Apache Spark, Hadoop, and Hive, supporting distributed processing, supply chain visibility, large-scale analytics, and business reporting across retail environments. Built enterprise data ingestion pipelines using Apache Kafka, REST APIs, and Databricks, enabling reliable acquisition of point-of-sale, inventory, ERP, and supply chain datasets across retail systems. Developed distributed processing workflows using PySpark, Spark SQL, and Apache Airflow, transforming high-volume retail datasets into analytics-ready formats supporting forecasting, reporting, and operational intelligence initiatives. Designed analytical repositories using Data Lake Architecture, Star Schema, and Hive, enabling dimensional modeling, data governance, efficient storage, and enterprise reporting across business domains. Developed customer analytics solutions using Scikit-Learn, K-Means Clustering, and Behavioral Analytics, enabling customer segmentation, targeted marketing, merchandising strategies, and actionable consumer intelligence initiatives. Built forecasting models using XGBoost, Time Series Forecasting, and Random Forest, supporting demand prediction, inventory planning, sales analysis, and operational decision-making across retail environments. Developed recommendation systems using TensorFlow, Collaborative Filtering, and Feature Engineering, improving product discovery, customer engagement, purchasing experiences, and retail intelligence across digital commerce platforms. Optimized analytical solutions using Hyperparameter Tuning, Cross-Validation, and Exploratory Data Analysis, improving predictive accuracy, model robustness, scalability, and business value across analytics initiatives. Developed executive dashboards using Tableau, Data Visualization, and Business Intelligence, enabling KPI monitoring, operational reporting, trend analysis, and informed decision-making across retail organizations. Environment: Python, SQL, Databricks, PySpark, Spark SQL, Apache Spark, Hadoop, Hive, Kafka, Airflow, Data Lake Architecture, Scikit-Learn, TensorFlow, XGBoost, Random Forest, K-Means Clustering, Time Series Forecasting, Feature Engineering, Tableau, Business Intelligence, Agile Scrum, JIRA, Git, Linux. Dr. Reddy's Laboratories | Hyderabad, India Aug 2015 Sep 2017 Role: Python Developer Responsibilities Developed manufacturing and regulatory reporting platform using Python, Django, and Flask, supporting pharmaceutical operations, compliance workflows, enterprise reporting, and production monitoring across manufacturing environments. Designed enterprise application architecture integrating SAP ERP, MES Systems, and LIMS Platforms, enabling seamless information exchange across manufacturing, laboratory, quality assurance, and reporting systems. Built ETL pipelines using Python, Pandas, and SQL, ingesting manufacturing, laboratory, and enterprise data from SAP, Oracle, CSV, and XML sources into reporting platforms. Developed data transformation frameworks using NumPy, Pandas, and Python OOP, enabling data cleansing, standardization, enrichment, and preparation for manufacturing analytics and compliance reporting. Designed relational database solutions using Oracle, PostgreSQL, and PL/SQL, supporting optimized schema design, stored procedures, transactional processing, and enterprise reporting workloads efficiently. Built ETL pipelines using Python, Pandas, and SQL, ingesting manufacturing, laboratory, and enterprise data from SAP, Oracle, CSV, and XML sources into reporting platforms. Developed data transformation frameworks using NumPy, Pandas, and Python OOP, enabling data cleansing, standardization, enrichment, and preparation for manufacturing analytics and compliance reporting. Designed relational database solutions using Oracle, PostgreSQL, and PL/SQL, supporting optimized schema design, stored procedures, transactional processing, and enterprise reporting workloads efficiently. Optimized database performance using SQL Query Tuning, Indexing Strategies, and PL/SQL Procedures, improving application responsiveness, reporting efficiency, transaction processing, and scalability across enterprise systems. Developed automation workflows using Linux, Shell Scripting, and Cron Jobs, enabling scheduled batch processing, report generation, operational efficiency, and reliable execution of manufacturing data pipelines. Environment: Python, Django, Flask, REST APIs, Oracle, PostgreSQL, SQL, PL/SQL, Pandas, ETL Pipelines, SAP ERP, MES, LIMS, Regulatory Reporting, Audit Trails, Linux, Shell Scripting, Cron Jobs, Git, Agile Scrum. Education: Lovely Professional University | India Computer Science Engineering, Bachelor s Keywords: continuous integration continuous deployment artificial intelligence machine learning javascript sthree database procedural language California Pennsylvania Washington