Renuka Sai Pothabattula

GenAI Chatbot Architecture & Development:

· Led design and development of GenAI-powered Medico-AI chatbots for oncology conference data, enabling intelligent medical query handling and evidence-based AI insights

· Architected and implemented RAG-based chatbot systems using AWS Bedrock, Azure OpenAI, and GCP Vertex AI for multi-cloud deployment

· Built conversational AI backends with advanced prompt engineering, context management, and multi-turn dialogue handling

· Developed intelligent query routing and intent classification systems using NLP and LLM fine-tuning techniques

· Implemented vector embedding pipelines and semantic search systems for efficient knowledge retrieval in chatbot responses

· Designed chatbot memory and context management systems for maintaining conversation state across sessions

Cloud-Native AI Infrastructure:· Designed and deployed scalable GenAI chatbot backends on AWS (Lambda, Bedrock, DynamoDB, S3), Azure (Azure Functions, Azure OpenAI, Cosmos DB), and GCP (Cloud Functions, Vertex AI, Firestore)· Implemented serverless architectures for AI chatbot workloads with auto-scaling and cost optimization across cloud platforms· Built hybrid cloud solutions enabling seamless AI model deployment and inference across AWS, Azure, and GCP· Developed API gateways and load balancing solutions for high-availability chatbot services· Implemented monitoring and observability systems using CloudWatch, Azure Monitor, and GCP Operations Suite

AI Data Engineering & Pipelines:· Built data engineering pipelines for ingestion, transformation, and fusion of large healthcare and research datasets into structured outputs for AI model training· Developed real-time data processing pipelines using Spark Streaming and Kafka for chatbot knowledge base updates· Implemented vector embedding pipelines and semantic search systems for RAG-based chatbot retrieval· Created data quality frameworks and validation systems for ensuring accuracy in AI chatbot training data· Built ETL pipelines for processing structured and unstructured data for chatbot knowledge bases

DevOps & Deployment:· Implemented Docker-based deployments for long-running AI jobs, ensuring scalability, maintainability, and efficient resource utilization· Developed CI/CD workflows for automated testing, deployment, and monitoring of GenAI chatbot systems· Created automated newsletters and reporting systems that delivered AI-driven insights to stakeholders· Established infrastructure as code practices for managing multi-cloud AI chatbot deployments· Implemented automated rollback and canary deployment strategies for AI model updates

Technical Leadership:· Led a team of backend developers in building production-grade GenAI chatbot systems· Established best practices for prompt engineering, model fine-tuning, and chatbot performance optimization· Mentored team members on GenAI technologies, cloud architecture, and AI chatbot development patterns· Conducted code reviews and technical design reviews for AI chatbot implementations· Collaborated with product managers and stakeholders to define AI chatbot requirements and success metrics

Enterprise AI Chatbot Development:· Led development of enterprise-scale AI chatbots for internal operations, customer support, and knowledge management· Architected multi-cloud GenAI chatbot solutions leveraging AWS Bedrock, Azure OpenAI, and GCP Vertex AI for high availability and disaster recovery· Built intelligent conversational interfaces with advanced NLP capabilities for technical documentation and operational queries· Developed domain-specific chatbots for energy sector applications with specialized knowledge bases· Implemented multi-language support and localization for global chatbot deployments

Cloud AI Platform Management:· Managed AI model deployment and inference infrastructure across AWS (Bedrock, SageMaker), Azure (Azure OpenAI, Azure ML), and GCP (Vertex AI, AI Platform)· Implemented cost-optimized AI chatbot solutions with auto-scaling and multi-region deployment strategies· Developed monitoring and observability systems for AI chatbot performance, accuracy, and user satisfaction metrics· Built automated alerting and incident response systems for AI chatbot infrastructure· Optimized AI model inference latency and throughput for real-time chatbot responses

Data Engineering for AI:· Built data pipelines for training and fine-tuning LLM models for domain-specific chatbot applications· Developed real-time data ingestion systems using Spark Streaming and Kafka for chatbot knowledge base updates· Implemented vector databases and semantic search systems for efficient RAG-based chatbot retrieval· Created data versioning and model versioning systems for tracking AI chatbot improvements· Built data quality and validation frameworks for ensuring chatbot response accuracy

Backend Architecture:· Designed scalable microservices architecture for AI chatbot backends with RESTful APIs and WebSocket support· Implemented caching strategies and optimization techniques for low-latency chatbot responses· Built authentication, authorization, and rate-limiting systems for secure chatbot access· Developed API gateways and service mesh solutions for managing AI chatbot microservices· Implemented circuit breakers and retry mechanisms for resilient AI chatbot services

Big Data & Analytics:· Processed and analyzed large-scale operational data using Spark, Hive, and cloud-native data processing services· Built real-time analytics dashboards for chatbot performance monitoring using Kibana, Tableau, and PowerBI· Developed data warehousing solutions using AWS Redshift, Azure Synapse, and GCP BigQuery for chatbot analytics· Created reporting systems for tracking chatbot usage, user satisfaction, and business metrics

AI Chatbot Development:· Developed production AI chatbots using AWS Bedrock, Azure OpenAI, and GCP Vertex AI for customer engagement and support· Built conversational AI systems with intent recognition, entity extraction, and context-aware response generation· Implemented RAG-based chatbot solutions with vector embeddings and semantic search capabilities· Developed multi-channel chatbot integrations (web, mobile, messaging platforms)· Built chatbot analytics and reporting systems for tracking user interactions and satisfaction

Cloud AI Integration:· Integrated AI chatbot services with AWS Lambda, Azure Functions, and GCP Cloud Functions for serverless deployment· Developed multi-cloud AI chatbot solutions with failover and load balancing across AWS, Azure, and GCP· Implemented cloud-native storage solutions using DynamoDB, Azure Cosmos DB, and GCP Firestore for chatbot state management· Built API integrations with third-party services and enterprise systems for chatbot functionality· Developed cloud infrastructure automation using Terraform and CloudFormation

Data Processing & Analytics:· Built ETL pipelines for chatbot training data preparation using Spark, Hive, and cloud-native data processing services· Developed real-time analytics dashboards for chatbot performance monitoring using Kibana, Tableau, and PowerBI· Implemented data quality and validation frameworks for AI model training datasets· Created data pipelines for processing customer interaction data and feedback for chatbot improvement· Built data warehousing solutions for storing and analyzing chatbot conversation logs

Backend Development:· Developed scalable backend APIs for AI chatbot integration using Python, Java, and Scala· Built microservices architecture for chatbot orchestration and multi-model AI inference· Implemented CI/CD pipelines for automated testing and deployment of AI chatbot systems· Developed database schemas and data models for chatbot conversation storage· Built integration layers for connecting chatbots with enterprise systems and databases

Big Data & Cloud Infrastructure:· Worked with Hadoop ecosystems (Cloudera, Hortonworks) for processing large-scale data· Developed data pipelines using Spark, Spark Streaming, and Kafka for real-time data processing· Implemented cloud migration strategies for moving on-premises data processing to AWS, Azure, and GCP· Built data lake solutions using AWS S3, Azure Data Lake, and GCP Cloud Storage.· Developed monitoring and alerting systems for data pipeline health and performance.

· Developed and maintained data engineering solutions for a leading chips and beverages company, focusing on sales and inventory analytics

· Designed and implemented data storage solutions using Power BI, Python, and SQL to support business intelligence and reporting

· Created comprehensive data analysis reports on sales performance and stock management for executive decision-making

· Established data pipelines to track and update key performance indicators (KPIs) and targets across organizational levels

· Delivered executive-level BI reports and dashboards to support strategic planning and operational insights

· Developed data engineering solutions for processing Lidar sensor data in NVIDIA's autonomous driving platform

· Built data pipelines for camera sensor integration, supporting computer vision model training and validation

· Engineered data infrastructure for steering and control system development, enabling real-time data processing for autonomous vehicle navigation

· Designed scalable data processing systems for multi-sensor fusion (Lidar, cameras, IMU) in autonomous driving applications

· Implemented data quality frameworks and validation pipelines for ensuring accuracy and reliability of sensor data in production environments

· Developed ETL pipelines for ingesting and processing large-scale sensor data from autonomous vehicle test fleets

· Created data annotation and labeling systems for training deep learning models used in autonomous vehicle perception

· Built real-time data streaming solutions for processing sensor telemetry during autonomous vehicle operations

· Collaborated with AI/ML teams to optimize data pipelines for model training and inference in autonomous driving systems

· Developed data warehousing and analytics solutions for storing and querying sensor data for research and development

· Created monitoring and alerting systems for tracking data pipeline health and sensor data quality metrics

· Worked as Associate Computer Vision Analyst on Google's autonomous vehicle projects, focusing on computer vision quality assurance and validation

· Served as Data Engineer and Computer Vision QA Analyst for Google Waymo's autonomous vehicle development program

· Developed and maintained data pipelines for processing Lidar sensor data from Waymo self-driving vehicles, enabling real-time perception and mapping capabilities

· Engineered data processing systems for camera sensor feeds, implementing computer vision algorithms for object detection, classification, and tracking

· Built data infrastructure for steering system development, collecting and analyzing telemetry data to optimize autonomous driving algorithms

· Designed and implemented ETL pipelines for ingesting, processing, and storing multi-modal sensor data (Lidar, cameras, radar) from autonomous vehicle fleets

· Developed data validation and quality assurance frameworks for sensor data accuracy and reliability in autonomous driving scenarios

· Created automated data processing workflows for sensor calibration, synchronization, and fusion across multiple sensor modalities

· Performed comprehensive data analysis on autonomous vehicle test data to identify patterns, anomalies, and performance metrics

· Collaborated with cross-functional teams including software engineers, ML engineers, and robotics specialists to deliver high-quality data solutions

· Implemented data annotation and labeling pipelines for training machine learning models used in autonomous vehicle perception systems

· Developed real-time data streaming solutions for processing sensor data during autonomous vehicle test drives and simulations

· Built data warehousing solutions for storing and querying petabytes of sensor data for model training and validation

· Created data visualization dashboards and reports for tracking sensor performance, data quality metrics, and autonomous driving system improvements