Real-time telemetry at scale
Architected AWS IoT and Kafka/MSK pipelines processing 1M+ daily events, cutting operational-visibility latency from minutes to sub-second across production facilities.
Senior Software Engineer · Cloud Platform · Event-Driven Systems · AI / Agent Platforms
Dev Patel
Cloud-platform and full-stack engineer with 5+ years architecting cloud-native, event-driven systems for high-volume dental manufacturing — AWS serverless, real-time IoT telemetry, a production AI agent platform, and reusable infrastructure that turns manual workflows into scalable, data-driven systems.
- Telemetry scale
- 1M+ daily events · minutes to sub-second latency
- Platform leverage
- Reusable patterns standard across 40+ backend services
- Operational reach
- Fleet analytics across 3,500+ machines
Serverless
IoT telemetry
Event-driven
Terraform IaC
Bedrock AI agents
Device OTA
What I Bring
I turn complex factory, device, and data systems into reliable software products.
My work sits where machines, cloud infrastructure, operations teams, and customer-facing systems meet: ingesting telemetry, designing backend services, automating environments, and shaping dashboards that make operational decisions faster.
Infrastructure other teams adopt
Established a standardized Terraform + Lambda delivery pattern — now the default across 40+ backend services — cutting new-service setup from days to hours.
AI & analytics that remove manual work
A production Bedrock AI agent platform for natural-language querying of operational data, plus fleet dashboards over 3,500+ machines with idle-detection alerting.
Engineering Shape
A full-stack engineer with depth in distributed cloud systems.
I am strongest in product-facing platform work: building the services, interfaces, and automation that connect edge devices, cloud infrastructure, internal users, and business outcomes.
IoT + telemetry
IoT Core, Greengrass, SiteWise, IoT Events, IoT Analytics
Event Layer
Kafka / MSK, Kinesis, WebSocket APIs, SQS, SNS
Cloud Services
Lambda, API Gateway, VPC, S3, DynamoDB, Timestream, Cognito, IAM
User Experience
React, MUI, real-time dashboards, reporting, alerts
Backend and integration
REST APIs, microservices, event contracts, cloud storage, data transformation, service orchestration.
Frontend product systems
Accessible dashboards, responsive UI, operator workflows, chart-heavy reporting, stateful interfaces.
Infrastructure and delivery
Standardized Terraform + Lambda patterns, multi-environment promotion, GitLab CI/CD, Kubernetes, deployment hygiene, production support.
AI-assisted workflows
Amazon Bedrock, planner-routed AI agent orchestration and tool-use, NL-to-SQL and natural-language querying over live operational data, prompt engineering.
Selected Systems
Production platforms I architected, built, and own end to end.
Where production details are confidential, these are framed by problem type, architecture, and capability — a scalable MES, an IoT device-management and OTA platform, a unified data-exchange hub, fleet analytics, and the telemetry, authentication, and AI infrastructure beneath them.
Scalable Manufacturing Execution System (MES)
An event-driven MES of 19 serverless microservices coordinating shop-floor production, spanning HTTP and WebSocket APIs, an IoT-driven Resource Manager, and an embedded AI layer.
- Resource Manager: optimization, DynamoDB-stream processing, IoT triggers
- AI layer: planner-routed Bedrock agent (Claude + Nova) with tool-use over live data
- Terraform-provisioned, multi-environment, deployed via GitLab CI
IoT Device Management & OTA Platform
The cloud control plane for a fleet of CNC milling machines — provisioning devices, pushing firmware and configuration over the air, and reconciling what each machine actually runs against what the cloud intends.
- Zero-touch X.509 provisioning with least-privilege per-device policies
- Over-the-air firmware and machine-config delivery
- Device-shadow reconciliation: hash-verified config, conflict detection, auto-recovery
Unified Data Exchange Platform
A central data-exchange hub moving real-time events between systems over WebSockets and IoT Core, with topic-based routing, webhook forwarding, a no-code message transformer, HIPAA-oriented PII protection, and a time-series ingestion path (Timestream for InfluxDB).
Fleet Analytics Dashboard
Production analytics for a milling fleet of 3,500+ machines — KPI reporting, idle-machine detection with alerting, an Entity Hub 360 view, and Zendesk / Pega field-service integrations — plus a cross-facility view unifying five facilities and nine production lines.
Real-time telemetry pipelines
AWS IoT and Kafka/MSK pipelines processing 1M+ daily telemetry events, cutting operational visibility latency from minutes to sub-second and giving production facilities real-time fault visibility for the first time.
Centralized authentication platform
A single reusable Lambda authorizer validating dual Cognito and Azure AD (MSAL) tokens plus API keys across HTTP, REST, and WebSocket APIs — standardizing secure access across internal operational applications.
Secure file ingestion infrastructure
Terraform-provisioned AWS Transfer Family SFTP into S3, automating secure ingestion from on-premise systems and removing manual transfers and a recurring source of ingestion errors.
3D Mesh QC Viewer
In-browser rendering of dental 3D meshes (STL / PLY / OBJ) with reference-vs-test geometric deviation heatmaps and PDF / zip export for quality control.
Experience
Built in production, grounded in fundamentals.
I started with computer science fundamentals, then moved into production engineering across cloud platforms, telemetry, dashboards, integrations, AI workflows, and deployment automation.
Sep 2021 - Present
Software Engineer III
Glidewell Dental
- Architected AWS IoT and Kafka/MSK pipelines processing 1M+ daily telemetry events, cutting operational visibility latency from minutes to sub-second and giving production facilities real-time fault visibility for the first time.
- Established a standardized Terraform + Lambda delivery pattern — now the default across 40+ backend services — cutting new-service setup from days to hours.
- Built a production AI agent platform on Amazon Bedrock — a planner-routed, multi-model tool-use agent with NL-to-SQL over operational data — cutting manual analyst and triage time.
- Designed an IoT device-management and OTA platform for CNC milling machines: zero-touch provisioning, over-the-air firmware/config delivery, and device-shadow reconciliation.
- Built the serverless analytics behind a fleet dashboard spanning 3,500+ machines, with idle-detection alerting that eliminated hours of manual status checks daily.
- Built a centralized authentication layer with dual Cognito and Azure AD (MSAL) validation across HTTP, REST, and WebSocket APIs.
- Led multiple legacy-JavaScript → React 19 migrations onto a shared MUI design system reused across 13+ internal dashboards.
2017 - 2021
Rutgers University - New Brunswick
Bachelor of Science in Computer Science
Toolbox
A stack broad enough for product delivery and deep enough for platform work.
Languages
Python, JavaScript, Node.js, Java, C#, C, C++, SQL
Frontend & auth
React, MUI, Angular, Highcharts / Plotly, three.js, Vanilla-JS SPAs, accessible dashboards; Azure AD / MSAL
AWS & data
Lambda, IoT Core, Greengrass, SiteWise, IoT Events/Analytics, Kinesis, Kafka/MSK, API Gateway (HTTP/WebSocket/REST), DynamoDB, Timestream/InfluxDB, S3, SQS, SNS, SES, RDS, EC2, VPC, Cognito, IAM, Amplify, Route 53
AI & delivery
Amazon Bedrock (Claude, Nova), AI agent orchestration & tool-use, NL-to-SQL over operational data, prompt engineering; Terraform, Kubernetes, Jenkins/CloudBees, GitLab CI/CD, Git, Azure, GCP
Contact
Let's build something durable, observable, and genuinely useful.
I am open to senior roles in cloud-platform, backend, and event-driven systems engineering — where AWS architecture, infrastructure leverage, real-time data, and AI-assisted automation matter.
devnandol@gmail.com Irvine, CA (551) 234-1189