ARYAN SAKHALA
██████╗ ██████╗ ██╗ ██╗ █████╗ ███╗ ██╗██╔═══██╗██╔══██╗╚██╗ ██╔╝██╔══██╗████╗ ██║███████║██████╔╝ ╚████╔╝ ███████║██╔██╗ ██║██╔══██║██╔══██╗ ╚██╔╝ ██╔══██║██║╚██╗██║██║ ██║██║ ██║ ██║ ██║ ██║██║ ╚████║╚═╝ ╚═╝╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝╚═╝ ╚═══╝ ███████╗ █████╗ ██╗ ██╗██╗ ██╗ █████╗ ██╗ █████╗ ██╔════╝██╔══██╗██║ ██╔╝██║ ██║██╔══██╗██║ ██╔══██╗███████╗███████║█████╔╝ ███████║███████║██║ ███████║╚════██║██╔══██║██╔═██╗ ██╔══██║██╔══██║██║ ██╔══██║███████║██║ ██║██║ ██╗██║ ██║██║ ██║███████╗██║ ██║╚══════╝╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝
$ cat ~/.config/aryan/profile.yaml
# ~/.config/aryan/profile.yamlname: "Aryan Sakhala"role: "Lead Software Engineer"location: "India -> Canada (2026)"focus:- "RAG-based Agentic Workflows"- "Multi-Modal AI Systems"- "Production ML Infrastructure"clients:- name: "Dell Technologies"type: "Enterprise AI"- name: "Intel Corporation"type: "CPU Optimization"- name: "Springer Publications"type: "Research"stats:years_experience: 5projects_delivered: 10+team_members_led: 7publications: 1supercompute_2024:event: "SC24 Demo"project: "Multi-Modal AI Showcase"status: "featured"
ARYAN SAKHALA - System Information===================================OS: Developer v5.0Uptime: 5+ yearsShell: /bin/engineerTerminal: AI/ML SpecialistPROCESSES RUNNING:PID NAME CPU MEM001 rag-workflows 95% HIGH002 llm-orchestration 90% HIGH003 team-leadership 88% MED004 research 85% MEDNETWORK:Dell Technologies [CONNECTED]Intel Corporation [CONNECTED]UNB Research [PENDING]
$ history --career
From Data Science to Lead Software Engineering, building AI systems for enterprise clients.
Research Assistant - PPML
University of New Brunswick / CIC - Canadian Institute for Cybersecurity
Privacy-Preserving Machine Learning Research
RAG Agentic Workflows for Dell & Intel | SC24 (SuperCompute) Demo | DTW 2024
AI HR Bot Development | Dashboard API Pipeline
Team Lead - Proofify (Blockchain) | IUDX Analytics (Gov. India)
B.E. Computer Engineering
Pune University | 2019-2023 | CGPA: 8.56
Skills → Projects
Each skill mapped to real projects. Hover tech tags to see exactly where and how each technology was applied.
Agentic RAG System
Enterprise document retrieval lacked contextual understanding, leading to irrelevant search results across large corpora.
Multi-agent orchestration with distributed task queues, vector similarity search, and LLM-powered reasoning chains.
Production deployment for enterprise clients with async pipeline processing
Contextual retrieval accuracy improved significantly with sub-second query response times across large document sets.
Computer Vision Benchmark Platform
No standardized way to benchmark CV inference across CPU (AMX/non-AMX) and GPU backends with real-time concurrency scaling.
Dockerized multi-backend system — OpenVINO async workers (AMX/CPU), GPU HTTP inference server, Celery coordinator, with real-time metrics via Prometheus.
Tested on Dell PowerEdge R770 (256 cores, dual NVIDIA RTX PRO 6000) processing concurrent video streams at scale
Identified and fixed critical memory leaks (5000-frame replication bug), oneDNN logging disk exhaustion, and Celery visibility timeout infinite-loop — enabling reliable multi-hour benchmark runs.
QAT Hardware Crypto Acceleration
TLS handshake throughput bottlenecked by CPU-bound RSA/ECDSA signing — limiting connections per second for vector database workloads.
QAT Engine/Provider plugged into OpenSSL → HAProxy TLS termination in front of Qdrant. Rustls PoC via patched async crypto provider for benchmark replication.
4x Intel QAT Gen4 devices on dual-socket Xeon 6760P, achieving 58K+ RSA-2048 signs/s (56x over software)
Successfully replicated Intel's benchmark chart (118% for ECDSA P-384). Built and patched C engine with weak symbol stubs for cross-version compatibility.
BBU-RRH Real-Time Logging System
Telecom BBU-RRH systems generated high-volume logs with no real-time streaming or structured analysis pipeline.
Kafka-based log ingestion with topic partitioning, consumer groups for parallel processing, and structured data transformation.
Real-time streaming across distributed telecom infrastructure
Enabled real-time log monitoring and structured analysis for telecom operations.
Player Performance Analytics
Sports analytics teams lacked automated player tracking and performance quantification from video footage.
YOLO-based detection pipeline with tracking algorithms, feeding into ML models for performance classification and statistical analysis.
Multi-video batch processing with real-time dashboard visualization
Automated player tracking and statistical performance analysis from raw video footage.
STT Benchmarking on Cloud
No reproducible benchmark framework for comparing speech-to-text models across different cloud GPU configurations.
Containerized Whisper inference pipeline deployed on AWS with parameterized GPU selection and automated metric collection.
Multi-GPU cloud benchmarking across various instance types
Reproducible STT performance baselines across cloud GPU tiers.
Production Web Applications
Need for modern, performant web interfaces for AI dashboards, e-commerce platforms, and portfolio showcases.
Next.js with SSR/SSG, React component architecture, TypeScript for type safety, and integrated payment systems.
Production-deployed applications serving real users
Responsive, SEO-optimized web applications with premium design and smooth interactions.