Srikanth – Python, Big Data, Microsoft Azure

Enterprise modernization initiatives focused on transforming legacy applications, implementing agentic AI solutions, and optimizing AI-driven code migration pipelines.

• Architected a cloud modernization strategy for a legacy application incurring $8M in annual infrastructure costs; built proof-of-concept components to secure stakeholder buy-in, driving an estimated $6M in annual cost savings;
• Developed an agentic AI solution capable of interpreting 30 years of legacy COBOL code, automating responses to complex business and technical queries and resolving critical subject-matter expertise bottlenecks;
• Engineered an agentic ticketing system that autonomously resolves 70% of inbound tickets, delivering $500K in annual operational savings;
• Standardized toolsets and skill combinations for AI coding agents to accelerate legacy-to-modern code migration, achieving an 80% reduction in LLM token consumption and lowering migration timelines.

A healthcare product that enables medical coders to review medical charts and complete the coding and processing workflow.

• Led multi-geographical teams in architecting and deploying GenAI-powered analytics platforms impacting $30M+ in annual revenue; embedded strict ethical AI frameworks and regulatory compliance while reducing user operational effort by 40%;
• Led the end-to-end migration of legacy data infrastructure to a modern Data Lakehouse architecture, resolving critical performance bottlenecks, consistently exceeding SLA targets, and cutting annual infrastructure costs by 55%;
• Spearheaded the transition to microservices-based API architectures for real-time analytics, reducing response times from 8 hours to under 1 minute and enabling 25% incremental revenue growth.

A healthcare Risk and Quality Analytics engine supporting risk analytics and HEDIS processing for healthcare organizations.

• Led 16 data engineers in developing an enterprise Risk Analytics engine supporting 40+ healthcare clients and 60M+ unique lives, directly enabling $100M+ in annual revenue;
• Directed an engineering team of 10 to construct an NCQA-certified HEDIS engine capable of batch and near-real-time analytics, driving $10M+ in revenue growth;
• Spearheaded the migration of legacy on-premises data pipelines to Azure, cutting data processing times by 80% and delivering $2.5M+ in annual infrastructure savings;
• Partnered across product and engineering teams to optimize cloud infrastructure and code execution, yielding $6M+ in annual operational savings.

A collection of early-career data engineering projects focused on data warehouse optimization, streaming data processing, data transformation, and storage cost reduction.

• Optimized Hive data warehouse performance through strategic partitioning and ORC format implementation, achieving a 50% reduction in query execution time and a 70% decrease in storage consumption;
• Developed a Spark streaming solution for near-real-time data mart updates, replacing daily batch processes and significantly improving data freshness;
• Led the migration of complex data transformation logic from standalone Java and DataStage to MapReduce jobs, reducing processing time by 70% and achieving over $2M in annual licensing cost savings;
• Implemented an automated data archiving utility, moving rarely used RDBMS data to object storage, reducing database size and generating $1M+ in annual savings while maintaining data accessibility.