Akshay Borse — Senior Software Engineer · AI infrastructure, distributed systems, developer platforms
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Senior Software Engineer with 8+ years at Microsoft and Amazon building AI infrastructure, distributed systems, security platforms, and developer tooling — including AI security for 237K+ agents across 13K+ organizations and event orchestration at 100K+ TPS.
Selected accomplishments from professional roles — not open-source repository statistics.
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AI agents protected |
TPS workload scale |
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Cloud cost reduction |
Microservices supported |
- AI-agent governance and risk enforcement across 13K+ enterprise tenants
- Platform standardization across ~50 services and 40 engineers, saving 40–50 engineering-weeks
- Deployment across commercial and US government cloud environments (GCC, GCCH, DoD)
AI security and governance for agentic identities in Microsoft Purview Insider Risk Management.
- Risk policy and enforcement for 237K+ active AI actors across 13K+ enterprise tenants
- Audit-history enrichment across 40+ global AD forests and three sovereign cloud regions
- Multi-region ingestion across 28 Azure regions processing 135K+ daily risk signals with compute isolation
Tech: C# · .NET · Azure Functions · Cosmos DB · Event Hubs · Kusto · Bicep · ARM · Microsoft Purview · Geneva
Platform engineering, distributed systems, production ML, and observability at scale.
- DSL-driven microservice bootstrap framework — weeks to hours setup, 40–50 engineering-weeks saved across ~50 services
- Event orchestration at 100K+ TPS across 16 microservices and 8 business-critical workflows
- SageMaker and Bedrock ML systems — $28M annualized delivery impact, 7% sales conversion lift, 13–16% fulfillment gains
- CloudWatch logging optimization — $250K+/month cost reduction; 60% fewer Sev-2 alerts
Tech: Java · Python · gRPC · REST · Docker · ECS · Lambda · DynamoDB · S3 · Aurora · Step Functions · App Mesh · Redis · Kinesis · CloudWatch · OpenSearch · SageMaker · Bedrock
Ad-Infinitum — F2FS Garbage-Collection Research
Linux filesystem research exploring F2FS garbage-collection policies through kernel instrumentation, performance benchmarking, and latency optimization. Published survey comparing NILFS and F2FS cleaning policies with compile-bench throughput analysis.
Concepts: F2FS · log-structured filesystems · garbage collection · kernel instrumentation · latency benchmarking
Interactive knowledge-graph platform for exploring cognitive patterns, behaviors, decisions, and feedback loops.
Java · C# · Python · JavaScript · SQL · C/C++ (systems & filesystem research)
Agentic AI governance · RAG · prompt engineering · Amazon SageMaker · Amazon Bedrock · Anthropic LLMs · production ML serving · quantile regression · XGBoost classification
Microservices · REST · gRPC · event-driven architecture · high-throughput orchestration · fault tolerance · horizontal scaling · Redis
Azure: Functions · Cosmos DB · Event Hubs · Kusto · Bicep · ARM · sovereign cloud (GCC, GCCH, DoD) · AWS: ECS · Lambda · DynamoDB · S3 · Aurora · Step Functions · App Mesh · Kinesis · Docker
Cosmos DB · DynamoDB · Aurora · S3 · event replay · vector search · multi-tenant data models
CloudWatch · Geneva · OpenSearch · Kibana · Kusto · telemetry standardization · dynamic log-level APIs · MTTR reduction
Unit & integration testing · load & stress testing · canary analysis · fault injection · automated rollback · CI/CD · infrastructure as code · TRA compliance
AI identity risk scoring · tenant-configurable policy enforcement · insider risk management · OAuth 2.0 · sovereign cloud compliance
Mermaid view
flowchart TB
AI["AI Infrastructure"] --> SEC["AI Security & Governance"]
AI --> ML["Production ML"]
SEC --> DS["Distributed Systems"]
ML --> DS
DS --> CLOUD["Cloud Platforms"]
CLOUD --> DEV["Developer Tooling"]
DS --> DEV
DEV --> DEL["Platform Delivery"]
CLOUD --> DEL
Interested in AI infrastructure, distributed systems, or developer platforms?
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Redmond, WA






