Saaim Abdullah
Introduction
Work Experience
Studies
Technical skills
Lahore, Pakistan
English
Urdu

Saaim Abdullah

Software engineer

I’m Saaim Abdullah, a software engineer based in Lahore, working European and US-East hours. I build distributed backends: services that hold up under traffic, pipelines that can be replayed, and data models that do not have to be apologised for a year later.

Two years of that so far. At Expertflow, a Cisco UCCE solution partner, I owned backend services for 1,000 to 3,000 daily users, deployed them across pods on AWS EKS with autoscaling driven by request throughput, and built event-driven ETL on Lambda, MWAA, PySpark and EMR. At Fitter Health I was the entire engineering function: a preventive health platform for a Spain-based team, taken from an empty repo to live production, with the Next.js frontend, the Django and DRF backend, a 106-table PostgreSQL schema, the AWS estate and the Odoo integration all mine, then documented and handed over.

Those two jobs taught opposite lessons and I needed both. In a team you learn that the contract between services is the real product. On your own you learn that nobody catches your bad data model, nobody notices the queue quietly dropping messages, and nobody documents the system if you don’t.

So I start from the failure modes, because the interesting part of a design is what it does when something is already broken. A queue that drops messages silently is worse than one that refuses them loudly, so the durable buffer and the dead-letter path come before the feature. A constraint in the database beats a check in the serializer, because the serializer is not on the path a management command takes. And I would rather argue about a schema for a day than migrate one for a week.

I’m now looking for a full-time role where I own a system rather than a ticket queue. I write up how I build things on Medium, the code is on GitHub, and my data structures and algorithms work is public in DSA-Python: graphs, dynamic programming, tries and the rest, written out with complexity analysis rather than pasted from a solutions page.

Work Experience

Fitter Health2026
Software Engineer, full-stack and cloudRemote, Spain-based team
  • Took a validated health-tech concept from zero to a live product used by patients and providers, as the only engineer. Designed and built the frontend, backend, database, cloud infrastructure, and data layer end to end, then documented and handed the system over.
  • Shipped the full AWS estate: ECS, RDS, S3, SNS, SQS, IAM and VPC, containerised, least privilege, and production-ready across two availability zones.
  • Defined every API contract and backend workflow, designing for reliability, maintainability, and data-driven features from day one.
  • Built the notification and file-handling architecture on Celery and Redis, and handed over a documented API surface backed by 135 passing tests, reversible migrations, and database-level integrity constraints throughout.
  • Modelled the membership and entitlement layer across a 106-table PostgreSQL schema: credit balances, coach and user session booking, an e-commerce store, an admin CRM, and invoice status synchronised with Odoo across more than one payment rail, under clinical compliance rules and a large role-based access matrix.
  • Designed and shipped the ETL pipeline into a star-schema PostgreSQL warehouse serving three consumers at once: feature workflows for the data science team, product analytics on best-selling items, and aggregated reporting for external partners. One modelled layer, so the three never disagree about what a number means.
  • Automated the member lifecycle email on EventBridge-scheduled Lambda and SES: renewal reminders that replaced a manual call-and-chase process, and inactivity re-engagement that runs without anyone triggering it.
  • Diagnosed silent notification loss in the inactivity re-engagement pipeline. SNS was fanning out directly to consumers with no durable buffer, so anything failing mid-delivery was gone with no error raised. Putting SQS between SNS and the consumers made delivery retryable and gave the failures somewhere to land.
Expertflow2024 to 2026
Software Engineer, Backend and CloudCisco UCCE solution partner, Lahore
  • Owned backend REST APIs serving 1,000 to 3,000 daily users, deployed as Docker containers on AWS EC2 behind an Application Load Balancer, with rate limiting, scoped IAM and VPC isolation.
  • Re-deployed the backend as a distributed system on AWS EKS, splitting services across pods with Horizontal Pod Autoscaler policies that scale replicas on request throughput, so traffic spikes cost capacity instead of availability.
  • Cut admin panel response time from 130 ms to 30 ms with Redis caching and database index tuning, after profiling showed the queries rather than the application were the cost.
  • Designed and shipped event-driven batch and streaming ETL pipelines on AWS Lambda, MWAA (managed Airflow), EMR, PySpark, Glue and S3, automating ingestion, standardisation and cataloguing of raw inputs into analytics-ready Parquet for BI reporting and ML feature workflows.
  • Developed a real-time voice and video communication SDK on WebRTC, and the REST APIs binding frontend clients, SDK modules and third-party platforms together.
  • Implemented a chatbot workflow that cut first-response time on inbound queries, delivered SQL-based analytics for customer insight reporting, and hardened platform access with time-bound permission controls.
  • Worked across the Angular frontend, backend and SDK teams to ship features end to end, which is where I learned that the contract between two services is the thing worth arguing about.

Studies

COMSATS University Islamabad, Lahore CampusBS Computer Engineering, 2021 to 2025. Coursework across data structures, databases, computer networks, operating systems, software engineering, computer architecture, and machine learning.
CertificationsAWS Certified Solutions Architect, AssociateAmazon Web Services, exam scheduled 2026AWS Cloud Technical EssentialsAmazon Web ServicesIntroduction to Data EngineeringDeepLearning.AI

Technical skills

Architecture & distributed systemsI decide what a system has to survive before I decide what it does, because that is what the design actually has to answer to. Services split across pods and scaled on throughput, fan-out buffered through durable queues with dead-letter paths, event-driven workflows, REST contract design, multi-tenant isolation, and medallion and star-schema data modelling.
  • System design
  • Event-driven
  • Kubernetes
  • Multi-tenancy
  • REST contracts
LanguagesI write backend, data and ML work in Python, and everything the user touches in TypeScript, so one person can carry a feature from the screen to the query. Strong SQL: window functions, query plans, and the indexes that decide whether either one finishes.
  • Python
  • TypeScript
  • SQL
  • JavaScript
Backend & APIsI build the part of a product nobody sees and everybody depends on: accounts, permissions, payments, bookings and integrations, designed to hold up as traffic grows. Django, DRF, FastAPI, Flask and Node.js/Express, including real-time features over WebRTC.
  • Django
  • Django REST Framework
  • FastAPI
  • Flask
  • Node.js
  • WebRTC
  • Celery
Data EngineeringI build both batch and streaming pipelines, and model the warehouse they land in, so reporting stops disagreeing with itself. Kafka into PySpark Structured Streaming, orchestrated with Airflow and MWAA, processed on EMR and catalogued with Glue, landing as a star schema over a medallion architecture that can be replayed when a definition changes.
  • Apache Kafka
  • PySpark
  • Apache Airflow
  • MWAA (managed Airflow)
  • Amazon EMR
  • AWS Glue
  • Star schema
  • Medallion
Cloud & DevOpsI set up somewhere for a product to live that won’t fall over, won’t leak data, and deploys itself from the repo. Production AWS across Lambda, EC2, ECS, EKS, S3, RDS, SQS and SNS, IAM, VPC and ALB, containerised with Docker, orchestrated on Kubernetes with autoscaling policies, and shipped through GitLab and GitHub CI.
  • AWS
  • Amazon ECS
  • Amazon EKS
  • Kubernetes
  • AWS Lambda
  • Amazon RDS
  • Amazon S3
  • Amazon SQS
  • VPC
  • IAM
  • Docker
  • GitLab CI
DatabasesI put the rules in the database rather than the form, so bad data cannot get in through a path nobody remembered. PostgreSQL as the default, including pgvector for retrieval, MongoDB and DynamoDB where the access pattern earns it, and Redis for caching and as a Celery broker.
  • PostgreSQL
  • pgvector
  • MongoDB
  • DynamoDB
  • Redis
AI & MLI build AI features you can check: a chatbot that answers from your own documents and shows where the answer came from, and recommenders that handle a brand-new user by design. RAG, multilingual embeddings and recommender engines with scikit-learn and TensorFlow, down to TensorFlow Lite Micro for on-device inference.
  • RAG
  • Recommenders
  • pgvector
  • scikit-learn
  • TensorFlow
FrontendI build the screens people actually use, so the product ships as one thing rather than a backend waiting on someone else. React and Next.js, Angular alongside the frontend team at Expertflow, with clean, accessible, responsive HTML and CSS.
  • React
  • Next.js
  • TypeScript
  • Angular
Testing & qualityI make it hard to break the thing quietly, which is what lets a product keep shipping after I hand it over. Automated test suites running in CI, database-level integrity constraints, clean reversible migrations, and structured logging with request tracing.
  • pytest
  • CI/CD
  • GitHub Actions
  • Migrations