Open to AI & Data Engineering roles
I'm an AI & Data Engineer passionate about engineering intelligent products from the ground up, from LLM-powered applications and RAG pipelines to backend architectures, data platforms, cloud infrastructure, and MLOps.
My work sits at the intersection of AI, software engineering, and data, where I transform ideas into scalable, production-ready systems.
Sources, pipelines & warehousing
Embeddings, RAG & agent orchestration
APIs, containers & cloud runtime
Metrics, alerting & retraining
Selected work
Systems taken from problem statement through architecture to measured outcome, across AI, data, backend, cloud, and MLOps.
2026
A long video is opaque: you cannot search it, skim it, or ask it a question. VidIQ converts speech, vision and structure into one queryable artefact so a viewer can jump straight to what matters.
2025
Urdu ASR systems often disagree on word boundaries, split/merge decisions, and OOV tokens. CORAL treats that disagreement as a signal, aligns model outputs, corrects lexical errors, and produces a cleaner transcript without retraining the acoustic models.
2025
Retail investors in Pakistan lack accessible, AI-driven tools to interpret market sentiment and news for informed decisions on PSX.
2026
City-scale crime data is large, continuously updated, and asked both strategic questions (long-horizon trends, hotspots) and tactical ones (unusual bursts). A Lambda architecture answers both without one compromising the other.
2026
Salary conversations in AI run on anecdote. This uses a first-party crowdsourced dataset to answer five concrete questions about pay by role, career level, region, company size and working mode.
2025
A model in production needs observability, alerting, and a route back to retraining when the data moves underneath it. Without those it is not deployed, it is merely running.
Profile
An AI and data engineer working across the whole path a system takes, from the pipeline that moves the data to the service that answers with it.
I build the layer between raw data and something a person can actually use: retrieval pipelines, LLM-backed services, and the backend and cloud plumbing that keeps them answering under real load.
Most of my work has started as a research idea and ended as a deployed system: an Urdu ASR correction pipeline that cut word error rate without retraining the acoustic models, an investment advisory platform built on LangGraph, an inference pipeline wired to Prometheus, Grafana and drift-triggered retraining.
I care about the unglamorous parts: clear interfaces, observable services, and delivery that stays incremental. Those are what decide whether a model still works six months after the demo.
Generative AI & LLMs
Prompt design, evaluation, and bounded refinement in production paths.
RAG systems
Chunking, embeddings, hybrid retrieval, and grounded answer synthesis.
Agentic AI
Stateful graphs, tool calling, and orchestration that stays debuggable.
Data engineering
Orchestrated ETL, warehousing, and data-quality checks that fail loudly.
Backend & cloud
Typed APIs, containerised services, and infrastructure as code.
MLOps
CI/CD for models, metric instrumentation, alerting, and retraining loops.
Experience
Applied AI engineering in industry, alongside teaching the systems and database courses that keep the fundamentals sharp.
Technical stack
63 technologies across 8 areas, grouped by where they sit in a system rather than by how well I claim to know them.
Day-to-day implementation languages, from application code down to systems work.
Model training, retrieval pipelines, and agent orchestration.
Model APIs I build against, chosen per task on cost and latency.
Service layers that stay predictable under real traffic.
Storage, distributed processing, and the cloud these systems run on.
Shipping, observing, and retraining what is already in production.
Turning a result into something a stakeholder can act on.
Where the work actually gets written and run.
Field notes
The rooms where the work happened: research labs, product teams, and the campus that started it.
01 / 04
Developed AI-powered phone call system, voice bots, and chatbots for urdx.tech. Built intelligent conversational AI systems that can handle natural language processing in multiple languages, enabling automated customer service and voice interactions. Created scalable voice bot infrastructure for handling real-time phone conversations.
Credentials
Verified coursework from Google, IBM, DeepLearning.AI, Vanderbilt, and Credly. Every entry links to its verification page.
Contact
Open to AI & Data Engineering roles. Whether it's a role, a build, or a problem worth thinking through, the inbox is open.
