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Senior AI Engineer

Python · AI infrastructure · Cloud DevOps

Senior AI & Python engineer — production-scale AI systems, enterprise RAG, agentic workflows, realtime platforms, and AWS-native delivery.

Years experience
6+
Engineering team lead
12+
Production applications
8+
AI platforms deployed
4+
Efficiency gains (voice path, CI/CD, infra)
~40%
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About

Production AI, backends, and cloud delivery

Six-plus years shipping AI-backed products and distributed systems—where retrieval quality, session semantics, and deployability matter as much as model choice.

I architect production-scale AI systems and cloud-native backends: enterprise RAG and semantic retrieval, realtime voice stacks (OpenAI Realtime, WebRTC), multi-agent orchestration (LangGraph, CrewAI), and the FastAPI / Django / PostgreSQL / Redis services that sit underneath them on AWS.

I have led a 12-engineer team while owning architecture reviews, CI/CD on Docker and ECS, and standards for monitoring and scalability. Recent work includes measurable wins such as faster voice order completion and higher deployment throughput through automation and containerized pipelines.

I care about explicit contracts between services, evaluable retrieval, idempotent tool execution, and infrastructure that stays operable after launch.

Focus areas

  • — AI infrastructure & production LLM features
  • — Enterprise RAG, embeddings, vector search
  • — Realtime voice & streaming architectures
  • — Multi-agent workflows & Redis-backed state
  • — AWS, containers, GitHub Actions, observability

Case studies

Production systems, documented end-to-end

Deep dives into architecture, tradeoffs, and measurable outcomes—not generic project tiles.

Skills

Stack across AI, backend, and infrastructure

Generative AI & AI systems

  • OpenAI APIs (Responses, tools, Realtime)
  • RAG & semantic retrieval
  • Agents & multi-agent workflows
  • LangChain · LangGraph · CrewAI
  • MCP servers
  • Structured outputs & tool calling
  • Prompt & context engineering
  • Evaluation pipelines
  • Embeddings & chunking
  • Realtime & streaming AI

Backend engineering

  • Python
  • FastAPI · Django · Flask
  • REST & GraphQL
  • AsyncIO & WebSockets
  • Microservices & event-driven design
  • Queues & background processing
  • API gateway & performance tuning

Cloud & infrastructure

  • AWS (ECS, EC2, Lambda, API Gateway, RDS, S3, IAM, CloudWatch, Amplify, EB)
  • Docker · Kubernetes
  • GitHub Actions & CI/CD
  • Nginx · Linux
  • Monitoring, logging, IaC patterns

Databases & AI storage

  • PostgreSQL · Redis · MongoDB · MySQL · MSSQL
  • Qdrant · ChromaDB · Pinecone
  • Vector search & caching

Frontend & integration

  • React · TypeScript · JavaScript
  • Angular · Redux
  • Realtime client integrations

Engineering practices

  • System design & architecture reviews
  • Performance optimization
  • Agile / Scrum · technical leadership
  • Testing (unit, integration) · TDD
  • Documentation & runbooks

Experience

Leadership at scale, with engineering depth

Roles where architecture, delivery, and team execution compound—shipping software that stays operable after launch.

  1. Xloop Digital Services

    Senior Software Engineer & Team Lead

    Nov 2022 — Present · Karachi, Pakistan · Full-time

    • —Lead engineering initiatives across AI infrastructure, production backends, and enterprise cloud while managing a 12-member engineering team.
    • —Architected and shipped 8+ production full-stack systems and 4+ enterprise AI platforms: RAG, agents, realtime AI, and distributed services.
    • —Built a production AI voice ordering platform on OpenAI Realtime API, WebRTC, FastAPI, Redis, and AWS—cut order completion time by 40%.
    • —Designed enterprise knowledge systems with semantic retrieval: LangChain, embeddings, vector stores, chunking, and contextual search tuning.
    • —Delivered multi-agent orchestration with LangGraph and CrewAI: memory, delegation, workflow graphs, and distributed execution backed by Redis.
    • —Owned microservices, async processing, event-driven flows, and high-concurrency API design; standardized reviews, CI/CD, and observability.
    • —Ran Dockerized workloads on ECS with GitHub Actions; improved deployment efficiency ~40% and reduced recurring infra toil.
    • —Shipped AI-backed HR analytics: insights, productivity signals, scheduling automation, and reporting pipelines on FastAPI and PostgreSQL.

    Python · FastAPI · Django · Flask · OpenAI APIs · LangChain · LangGraph · CrewAI · Redis · PostgreSQL · AWS · Docker · ECS · GitHub Actions · Qdrant · ChromaDB · React · TypeScript · WebRTC

  2. Changes on the Fly

    Full Stack Developer & Cloud Engineer

    Jan 2025 — Present · Toronto, Canada (Remote) · Contract

    • —Enterprise modernization: cloud migration, infrastructure automation, and deployment optimization for large-scale backends.
    • —Migrated legacy PHP from Linode to AWS with Dockerized pipelines and automated CI/CD.
    • —Built and maintained Angular + PHP applications on AWS with scalable service integrations.
    • —Implemented zero-downtime deploys, automation scripts, and monitoring for production reliability.
    • —Tuned AWS around EC2, RDS, API Gateway, Amplify, and Lambda for cost and performance.

    Angular · PHP · Docker · AWS · EC2 · RDS · Lambda · API Gateway · GitHub Actions

  3. AKSIQ Technologies

    Senior Software Engineer

    Jan 2022 — Oct 2022 · Karachi, Pakistan · Full-time

    • —Banking and fintech backends: fraud-adjacent workflows, transaction monitoring, and hardened Django/Flask services.
    • —Led backend initiatives with audit-friendly logging and strict transactional boundaries.
    • —Containerized services and CI/CD; cut manual deployment overhead ~50%.
    • —Automated data collection and analytics with Selenium and Python pipelines on AWS.

    Python · Django · Flask · Docker · PostgreSQL · Selenium · CI/CD · AWS

  4. Linkstar

    Python Developer

    Sep 2020 — Dec 2021 · Karachi, Pakistan · Full-time

    • —Backend and automation for production web apps: Flask APIs, React integration, and deployment pipelines.
    • —Freelancing platform backend in Flask with PostgreSQL.
    • —Selenium-based extraction and scraping systems with managed deploys on Heroku.

    Python · Flask · React · Selenium · PostgreSQL · Heroku · CI/CD

Background

Education & certifications

Formal training and credentials alongside production engineering work.

Education

Bachelor of Computer Engineering

Mohammad Ali Jinnah University · 2018–2022

GPA 3.46. Coursework in artificial intelligence, database systems, algorithms and data structures, software engineering, IoT, and distributed computing.

Certifications

  • —Cloud Native Developer — Emeritus
  • —IoT Developer — PIAIC
  • —Web Development Using Python & JavaScript — Harvard Online

Languages
English (fluent) · Urdu (native)

GitHub & engineering

How I think about shipping AI in production

Building reliable AI systems is more than integrating APIs — it requires orchestration, observability, scalability, latency optimization, and thoughtful infrastructure design.
  • Orchestration: explicit graphs and state machines over opaque agent loops when audits and rollback matter.
  • Observability: trace IDs across HTTP, queues, and model calls; structured logs for retrieval and tool payloads.
  • Scalability: separate hot paths from batch analytics; cache where correctness allows; load-test voice and RAG separately.
  • Latency: colocate services, trim schemas, stream partials, and measure p95—not averages—on customer-facing paths.
  • Infrastructure: least-privilege IAM, secrets rotation, environment parity, and runbooks that match how incidents actually unfold.

GitHub activity

333+ contributions in the last year

github.com/AbdulWasey2211002TRACND →

Contact

Direct channels

Senior AI / backend / platform roles, contract or full-time—especially teams shipping RAG, voice, or agentic products on AWS.