5×
Scaled enterprise data throughput
Led a governed Databricks Lakehouse with 200+ Spark, Airflow, and Python pipelines, enabling fivefold throughput across business-critical analytical workloads.
Platform strategy · Cigna / eviCoreLead Engineer, Data & AI Infrastructure · Author · Speaker
Engineering data and AI infrastructure from prototype to production.
I build and lead high-throughput data platforms, agentic pipelines, RAG systems, and AI automation that turn complex enterprise data into reliable products, from petabyte-scale analytics to governed, production-grade intelligence across regulated and high-scale environments.
BuildDeployIntegrateScaleIEEE Senior Member
Executive impact
Accomplishments with operational weight.
Evidence-led outcomes across platform scale, performance, adoption, governance, and enterprise delivery.
45%
Accelerated analytical performance
Reduced query time through Delta Lake Z-ordering, adaptive tuning, and workload-specific performance engineering.
Performance engineering70%
Turned AI access into adoption
Drove targeted enablement across Meta design teams, improving tool adoption and strengthening day-to-day creative workflows.
AI transformation · Meta75%
Industrialized cloud delivery
Architected infrastructure-as-code for Databricks and storage, sharply reducing setup time while improving deployment consistency.
Azure · Terraform · CI/CD500+
Governed sensitive access at scale
Built Spark SQL row-level security for regulated data, enabling secure multi-tenant analytics access with zero reported breaches.
Enterprise security · Governance$10M
Connected analytics to cost impact
Delivered KPI and operational intelligence that improved decision visibility and supported documented cost savings.
Enterprise analytics modernizationWhat I build
AI systems that operate in the real world.
End-to-end engineering across data foundations, model context, workflow orchestration, evaluation, deployment, and operational feedback loops.
Agentic pipelines
Multi-step, tool-using workflows with durable orchestration, state and memory management, human checkpoints, retries, guardrails, and production observability.
Agents · Tools · Memory · Orchestration
RAG & knowledge systems
Grounded AI experiences built on ingestion, chunking, embeddings, hybrid retrieval, re-ranking, citations, access control, and evaluation-driven quality.
Retrieval · Vector search · Grounding · Evals
Intelligent automation
Event-driven automation that connects models to enterprise systems, turns manual processes into governed workflows, and keeps operators in control.
APIs · Events · Automation · Human-in-the-loop
Production AI infrastructure
Secure, scalable foundations for batch and real-time inference with data quality, lineage, CI/CD, latency and cost controls, monitoring, and auditability.
Platform engineering · LLMOps · Reliability
Discover the workflow→Build the data foundation→Deploy the intelligence→Measure and improve
Why this work matters now
Positioned where the enterprise is moving.
Market signals reinforce the need for scalable data foundations, production AI, governed automation, and leaders who can move systems safely beyond experimentation.
86%
of surveyed employers expect AI and information processing to transform their business by 2030.
World Economic Forum · 2025 ↗88%of surveyed organizations report regular AI use, while most still struggle to scale.
McKinsey State of AI · 2025 ↗63%of breached organizations lacked an AI governance policy or were still developing one.
IBM Cost of a Data Breach · 2025 ↗73%of surveyed employers plan to accelerate process and task automation.
World Economic Forum · 2025 ↗Experience
A career built from the platform up.
Progression from hands-on BI engineering to enterprise architecture, platform leadership, and AI-scale data systems.
Lead Engineer, Data & AI Infrastructure
Building high-throughput data pipelines, AI infrastructure, automation, and analytics foundations supporting product and intelligent-system initiatives at petabyte scale.
Cigna GroupManager, Enterprise Data Office
Led enterprise lakehouse architecture, ML-ready data layers, cloud modernization, secure access, and 10TB+ daily processing.
Senior BI Developer
Modernized 15TB enterprise warehouses and built 50+ KPI dashboards serving 200+ analytics users.
BI Developer
Delivered retail data warehouses and real-time reporting for global brands, improving forecast accuracy and data consistency.
Education
Doctoral research · In progressDayananda Sagar University
PhD Research, Computer ScienceMaster’s degreeUniversity of Hartford
MBA, Business AnalyticsBachelor’s degreeVisvesvaraya Technological University
Bachelor of Engineering, Electronics and CommunicationCore expertise
Agentic AI PipelinesRAG & Knowledge SystemsData & AI PlatformsLakehouse ArchitectureWorkflow AutomationData ReliabilityDistributed SystemsAI EvaluationData GovernanceCloud ModernizationTechnical Leadership
Python · PySpark · SQL · Databricks · Delta Lake · Apache Spark · Kafka · Airflow · Azure · Terraform · Kubernetes · Vector Search · LLMOps · Power BI
Doctoral research · In progress
Trustworthy self-healing for cloud-native data pipelines.
PhD Student in Computer Science at Dayananda Sagar University, researching how generative AI can diagnose and propose repairs while policy, testing, auditability, and human oversight retain execution authority.
Policy-Governed Agentic Framework for Explainable Diagnosis and Safe Self-Healing of Cloud-Native Data Pipelines
Evidence→Context→AI reasoning→Policy gate→Verify
The design-science study compares rules-only, AI-only, and governed-agent approaches across diagnosis accuracy, recovery, safety, and human usefulness. Expected outputs include a vendor-neutral framework, incident representation, benchmark, prototype, and measured evidence.
Research principle: generative AI proposes; policy and accountable people decide what is allowed.Research & authorship
Applied research for complex systems.
15 publications spanning data and AI architecture, secure pipelines, edge computing, predictive analytics, and applied machine learning across multiple domains.
View complete Google Scholar profile ↗3 booksIntro to Data Science for HealthcareHealthcare Data Pipeline MasteryBuilding Public Health Data Systems
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Zero-ETL Architectures for AI Workloads
IEEE · ICOCT 2025
Optimizing Healthcare Pipelines for Patient Benefit
Springer · 2025
Edge Computing for Healthcare IoT
IEEE · 2nd Best Paper · 2025
Enhanced ECG Classification Using Transformer–CNN
IEEE · ICIMIA 2025
Predictive Analytics for Preauthorization Outcomes
Springer · 2025
Speaking & influence
Translating systems into ideas people can use.
Keynotes and invited talks across enterprise data engineering, governed AI, multi-agent systems, platform reliability, and responsible automation.
The Prior Authorization Pipeline
DAMA San Francisco · Secure Data & AI Forum
From Reactive to Predictive
Fremont ACM Chapter
Beyond Generative AI
ICMDIA-25
Engineering the Backbone
International Conference on Computer Science
45+documented peer, journal & conference reviews
4fellowships and senior professional distinctions
3Professor of Practice appointments
2patent and registered-design initiatives
Connect
Data platforms should create confidence, not complexity.
Start a conversation ↗
Speaking · Research · Advisory · Collaboration