Technology leadership / Applied AI
I’m Srini Venkataraman. I bring together technology leadership, enterprise architecture, and software engineering to turn complex business problems into practical technology solutions. My work spans enterprise applications, data platforms, and applied AI.
Selected work
Five projects spanning clinical intelligence, healthcare interoperability, applied AI, utility operations intelligence, and energy analytics. Explore each project’s purpose, architecture, and technical approach, with source code available on GitHub.
A physician-centered clinical intelligence workspace unifying encounters, labs, medications, PDFs, portal messages and DICOM imaging on a governed Databricks lakehouse, with evidence-grounded AI.
Connect standards-based healthcare data with medication safety analysis using FHIR.
Analyze medication and lab data to identify potential safety concerns and provide traceable explanations.
Combine reliability metrics, asset operations, document retrieval, and grounded AI in a utility operations application.
Explain changes in electricity usage through data analysis, weather context, and AI-assisted insights.
About me
My experience spans enterprise application development, architecture, engineering leadership, and technology strategy. I’ve led engineering teams, worked alongside data and analytics professionals, and guided software initiatives from planning and design through delivery and ongoing support.
I take a technology-agnostic approach, choosing tools and platforms that address business needs while aligning with enterprise standards and existing systems. Alongside my advisory work at Open Sky Health, I stay actively involved in hands-on development, building projects in data engineering, cloud platforms, and applied AI. This work keeps me learning and informs how I approach architecture and technology decisions.
Open Sky Health
April 2018–Present
Lead product and technology strategy across roadmaps, architecture, workflows, partnerships, and implementation planning.
UAB Medicine
April 2001–April 2024
Led engineering teams and directed enterprise application development, systems integration, and modernization across hospital and ambulatory settings.
12+ enterprise applications · 30+ EHR enhancements · 10,000+ users supported
Education
MBA, University of Alabama at Birmingham
Master of Computer Applications, Bharathidasan University
BSc in Chemistry, Bharathidasan University
Career impact
Led and directed application development supporting 10,000+ users across hospital and ambulatory settings during my tenure at UAB Medicine.
Application development, integration, and modernization
Improved information access and clinician workflows
Connections with consumers, external providers, and external organizations
Adoption of a mobile EMR application
iPad surgical-consent platform
Integrated medical-device delivery platform
Selected accomplishments during my UAB Medicine tenure. Application categories may overlap.
Expertise
Engineering leadership, team development, product and technology strategy, stakeholder alignment, architecture governance, and Agile delivery.
Enterprise architecture, application modernization, APIs, and systems integration. Java, Spring Boot, React, React Native, RabbitMQ, HL7/FHIR, AWS HealthLake, and Oracle Health/Cerner integration.
Data engineering, analytics, cloud data platforms, AI Search, hybrid RAG, and AI-assisted explanations grounded in source data. Python, SQL, Databricks, Unity Catalog, MLflow, Vector Search, and Amazon S3.
Secure development, maintainable software, automated testing, and reliable delivery through DevOps and CI/CD.
What comes next
My next projects will explore how modern data platforms, analytics, and AI can support financial-services workflows such as fraud investigation and commercial credit risk, using synthetic data and source-grounded explanations.
Financial Crime Intelligence: Explore transaction monitoring, anomaly detection, relationship analysis, fraud investigation workflows, and grounded AI explanations across synthetic banking data.
Commercial Credit Early Warning Intelligence: Explore borrower risk, credit deterioration, covenant monitoring, portfolio concentration, and AI-assisted relationship-manager briefings using synthetic commercial banking data.
Explore my GitHub →