Hamid Raza — Lead Engineer · Distributed Systems, Real-Time Data & AI
I design the backend and AI systems that businesses in logistics, mining, fleet, healthcare, and analytics rely on to turn real-time operational data into decisions — reliably, at scale, and at controlled cost.
Selected work
The work that matters most rarely lives in public repos. Here is the scope, operating context, and outcomes behind the platforms I have architected and led.
Operanex
Real-time mining and fleet operations platform that ingests IoT and telemetry from trucks, excavators, and field devices, then turns it into decisions operators can trust.
Role
Backend platform architecture, telemetry ingestion, device data normalization, and operator-facing product delivery.
Impact
glasc.io
Real-time fleet tracking and analytics — vehicle movement, weight, location, and event detection for logistics visibility and payload theft alerts.
Healthcare platform
Electronic Health Record and operations platform for US/CA providers: records, scheduling, telehealth, charting, billing, RCM, and eligibility.
glasc.io
Centralized access control and authorization for mining and logistics environments — identity, role-based access, and auditability.
glasc.io
Multi-tenant authentication platform: SSO, MFA, OAuth, and user management across a suite of business applications.
Earlier: field-force analytics at aiSight.ai, marketplace platform work at Graana.com, and cloud automation at Cloud Rangers and uExel.
GitHubExperience
The common thread is operational consequence — products where architecture quality and engineering clarity carry measurable cost.
Operanex
Sep 2024 — Present
Brussels, Belgium
Own backend services, platform architecture, and end-to-end data flow for IoT and analytics products.
glasc.io
Jan 2022 — Sep 2024
Johannesburg, South Africa
Led technical roadmap and backend architecture for SaaS across access control, fleet tracking, weight analytics, and logistics.
glasc.io
Oct 2019 — Dec 2021
Johannesburg, South Africa
Built platform and product features for real-time fleet management and telemetry-heavy workflows.
Earlier
aiSight.ai
Senior Software Engineer
Backend services and real-time dashboards for FMCG analytics used by 500+ enterprise clients.
Graana.com
Full Stack Engineer
Listing and search workflows for a real estate marketplace; 50k+ listings, 40% faster page loads.
Cloud Rangers
Backend Engineer
REST APIs and cloud automation, cutting manual operational effort by 40%.
uExel
Backend Developer
Backend services and APIs supporting 5,000+ daily requests.
Capabilities
I reason across architecture, AI, product, and infrastructure — and stay close enough to the code to keep the trade-offs honest and the delivery on track.
Node.js / TypeScript services, clear boundaries, data contracts, and APIs that stay understandable as scope grows.
RAG pipelines and multi-agent workflows — retrieval, tool-calling, and orchestration — with evals and guardrails so LLM systems run reliably in production.
Event-driven flow across Kafka, MQTT, and RabbitMQ into PostgreSQL, MongoDB, Redis, and Cassandra.
Services and integration layers across NestJS, Express.js, REST, GraphQL, and tRPC on AWS.
Integrating device data, normalizing telemetry, and improving data accuracy across operational workflows.
Fleet, mining, analytics, and healthcare products with live dashboards, alerts, and secure workflows.
Mentoring, reviews, and stakeholder alignment that ties technical choices to product and client goals.
Tools I reach for
Languages & runtime
Backend & APIs
AI & LLM
Interfaces
Data & messaging
Infrastructure
About
I'm a lead engineer who builds the systems businesses rely on to make decisions in real time — across logistics, mining, fleet, healthcare, and analytics.
My core is backend architecture and data flow: event-driven telemetry pipelines, IoT integrations, and platform decisions that stay tied to cost and business outcomes. I work directly with clients and product teams — not around them — turning ambiguous requirements into systems that scale responsibly.
I build agentic AI directly into those platforms: RAG pipelines and multi-agent workflows that let LLMs reason over operational data, with the evals and guardrails to run them reliably in production.
B.S. Software Engineering — UET Taxila, 2011–2015.
Ingestion, normalization, storage, and workflows have to agree before a real-time system can be trusted.
The highest-leverage work sits where client needs, product goals, and backend architecture meet.
Clear reasoning, early trade-offs, code and architecture review — so teams ship reliably as complexity grows.
Contact
Strongest fit: lead engineering roles across backend architecture, agentic AI, and real-time data systems — or consulting where the first win is making a complex system clear and the path forward obvious.