SREERAMA MURTHY KANTU

• Company Overview: SaaS-based product development organization behind the NSLHUB iBPMS Product Platform
• Led sprint planning, user story reviews, and managed scope creep, while facilitating daily Scrum meetings to address development bottlenecks
• Created process flows, wireframes, and other artifacts to support product development using modern tools; maintained alignment between user stories, business objectives, and product vision
• Worked with Product Managers, UX Designers, Dev Teams (Data Engineers, Data Scientists, Product Engineers), and stakeholders to translate customer insights into user stories, ensuring clarity for software and QA engineers
• Analyzed large datasets to uncover trends, patterns Worked with Business, Product, and Data Engineering teams to improve data accuracy and deliver actionable dashboards and visualizations
• Automated data processes to streamline data ingestion, curation, analysis, and integration
• Assessed project scope, identified implications, and ensured comprehensive understanding across teams, guiding Data Analysts to align business needs with appropriate algorithms
• Converted business needs into technical specifications for Data Lake, Data Warehouse, or Data Mart solutions, creating flexible data models that facilitate insights and Business Intelligence
• SaaS-based product development organization behind the NSLHUB iBPMS Product Platform
• Established strong relationships with key stakeholders, fostering collaboration across departments for seamless product development.

• Leveraged cutting-edge AI and advanced computing technologies to elevate security and operational efficiency across various domains, including Smart Home, Smart Retail, Smart City, and Smart Warehouse, through sophisticated Smart CCTV surveillance solutions
• Leveraged AI-driven analytics, machine learning, and Deep Learning for advanced object detection, facial recognition, and anomaly detection
• Implemented a microservices architecture for flexibility and scalability, allowing independent development and deployment of AI features
• Integrated advanced video analytics for real-time processing and analysis, including motion detection, activity recognition, and pattern analysis
• Employed Event Stream Processing with Kafka and Power BI for real-time reporting, alerts, and actionable insights
• Enabled client-specific customization of AI microservices, with dynamic resource allocation to handle varying workloads
• Established CI/CD pipelines for automated build, test, and deployment processes, ensuring rapid updates and system stability
• Key Technologies: TensorFlow, OpenCV, Kafka, Power BI, H.265, HEVC, JIRA, Confluence

• Spearheaded the development of the Intelligent Workflow Recommender (IWR) system to enhance business process optimization across various domains
• Conducted extensive data synthesis, cleansing, and extraction to prepare unstructured datasets for model training
• Integrated various data sources and utilized advanced techniques to enhance the system's intelligence
• Implemented Deep Learning models and incorporated Generative AI to enable contextual semantic searches and autonomous solution generation
• The system was designed to provide intelligent, context-sensitive recommendations and streamline solution design
• Defined the product vision and led the development of a scalable RAG platform, achieving an 8% improvement in precision and recall through multi-index search, hybrid retrieval models, and embedding fine-tuning.
• Enhanced query processing with intent detection, query decomposition, and intelligent routing to optimize retrieval efficiency.
• Improved generative AI responses using advanced prompt engineering, response validation, and RAGAS metrics to ensure coherence and relevance.
• Designed user feedback systems and implemented streaming strategies leveraging chunking, custom parsers, and intelligent routers to reduce latency and enhance user engagement.
• Conducted trade-off analyses of retrieval and generation architectures, balancing accuracy, latency, and cost while supporting large-scale deployment.
• Deployed the IWR system across various environments, delivered impactful, intelligent solutions tailored to diverse industry needs, including Finance, US Healthcare, and Human Resources.
• Encapsulated the integration of Generative AI and Large Language Models (LLMs) with recommender systems to elevate and personalize user experiences

Technologies: Python, LangChain, Chroma, Pydantic, LlamaIndex, LlamaPar, Custom Parsers, MySQL, FastAPI, JIRA, Confluence, Power BI, Excel

• Engineered an AI-driven prediction system on the NSL Platform to enhance demand planning, sales forecasting, and seasonal planning for manufacturing and retail distribution
• Utilized transactional data and implemented a Hadoop Druid-based Data Store (including Data Warehouse and Data Marts) to manage and organize the data
• Developed Python-based integration scripts to process the data and deliver actionable insights
• Created multi-level sales forecasts and enabled dynamic drill-up/down reporting for detailed analysis
• Successfully deployed the system, providing enhanced forecasting capabilities and reporting functionalities that support strategic decision-making in manufacturing and retail distribution
• Developed comprehensive financial models for effective budgeting and resource allocation, leading to cost optimization.
• Key Technologies: Python, Hadoop, Hive, Kafka, Superset, TensorFlow, Airflow, Power BI, JIRA, Confluence

• Company Overview: Sutherland is a leading US Healthcare business process transformation company with over $750 million USD in revenue, specializing in rethinking and rebuilding business processes for the digital age using Auto IT scripts and Frameworks
• Led the Sutherland Insurance Automation Practice for U.S.-based healthcare payors, including Blue Cross Blue Shield and Humana
• Directed the automation of batch processing for electronic claims, the automation of provider demographic data extraction and validation, eliminating manual processes leveraging the Auto IT framework to achieve a productivity improvement of over 20%
• Developed a database-driven solution to automate call routing by analyzing patterns such as time zones, agent skills, and availability, resulting in substantial productivity gains and optimal resource utilization
• Conducted comprehensive analyses to identify the root causes of claim processing errors and created a classification database
• This initiative improved automation addressed chokepoints, exceeding SLA requirements
• Implemented analytics across claims management, including production, quality, and revenue
• Developed and distributed dashboards for stakeholders and accomplished multiple tasks within established timeframes.
• Key Technologies used: MS Office, Power BI, Auto IT, IDB