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Currently available for select engagements

Hire AI Developer for Media Agencies
Senior, direct, no layers

An AI developer builds practical machine-learning features into the systems media agencies already run: recommendation and personalisation, content tagging and search, demand forecasting, and document processing.

15+
Years Experience
100+
Projects Delivered
6
Countries Served
$25M+
Revenue Enabled
Media Agencies — AI Developer

A senior ai developer for media agencies

Media agencies live or die on two things: buying well and proving it. Clients now expect every dollar of spend to be traceable to an outcome, and they expect the report before they ask for it. Meanwhile the buying itself has fragmented across programmatic, social, search, audio, and out-of-home, each with its own platform, its own pacing quirks, and its own definition of a conversion.

The operational cost is what quietly eats margin. Planners re-key insertion orders, account managers rebuild the same report in a different format for each client, and trafficking errors — a wrong creative, a missed flight date, a budget that paced too fast — surface only when the client spots them. Agencies that fix this plumbing keep clients longer than agencies that just buy cheaper.

An AI developer builds practical machine-learning features into the systems media agencies already run: recommendation and personalisation, content tagging and search, demand forecasting, and document processing. The work starts from a real bottleneck — a catalogue nobody can search, an audience nobody can segment, a forecast built on gut feel — and ends with a model in production, measured against the manual process it replaced, not against a demo. I am Omer Muneer Qazi, with 15+ years of experience and 100+ projects delivered. Browse the full hire hub for related engagements, or book a free discovery call and we will test the fit before any scope is agreed.

What you get

What this engagement covers for media agencies

  • Applied ML, not research projects — models scoped to one decision your team already makes, with training data you already own and a clear owner for accuracy after launch.
  • Search and discovery — semantic search and auto-tagging across catalogues, archives, and content libraries, so material surfaces by meaning instead of by exact keyword.
  • Forecasting and planning — demand and pacing models built on your own history, used to plan campaigns, releases, and inventory rather than to impress in a slide deck.
  • Document and data processing — extraction and classification that turns contracts, briefs, and reports into structured data your other systems can use.
  • Production deployment — models served behind your existing tools with logging, fallbacks, and retraining triggers, so accuracy is monitored instead of assumed.
  • Evaluation before trust — a test set drawn from your real cases, scored before launch, so the team knows exactly where the model is reliable and where a human stays in charge.
Working in Media Agencies

Built for how media agencies actually work

Tools and systems. Media work has its own established stack, and new builds have to plug into it. Buying and trafficking run through Mediaocean's Prisma for many agencies; audience and reach planning leans on Nielsen data and Comscore; reporting is assembled in AgencyAnalytics, Tableau, or Looker Studio; day-to-day coordination lives in Asana, Monday.com, or Slack; verification comes from DoubleVerify or Integral Ad Science. Any system that cannot read from and write to these tools will be ignored by the people doing the actual buying.

Where the work pays off. The work that moves the needle is operational: one intake for a campaign brief that flows into trafficking without re-typing; pacing alerts that flag overspend before the client does; a single reporting layer that pulls platform numbers into the client's own vocabulary; and a clean archive of plans, creatives, and results so the next brief starts from evidence instead of a blank document. Buying skill stays with the planners; the machinery around it stops leaking time and margin.

How it works

From kickoff to delivery

  1. Find the decision

    Pick one repeated decision the model will improve, and agree how success will be measured against the current manual process.

  2. Audit the data

    Check what you actually have, how clean it is, and what is missing — then scope the model to the data that exists, not the data you wish you had.

  3. Build and evaluate

    Train on your history, score against held-out real cases, and fix the failure modes before anyone depends on the output.

  4. Deploy and monitor

    Ship behind your existing tools with logging and accuracy tracking, and retrain on a schedule tied to how fast your world changes.

Why Omer

Why hire Omer

  • Senior-only delivery: one person owns the ai developer work, its integrations, and its handover.
  • Built for production, not demos: permissions, monitoring, and documentation from day one.
  • Direct communication: no account-manager layer between you and the person doing the work.
  • Measured on outcomes: agreed metrics reviewed regularly, with scope adjusted on evidence.
  • No lock-in: your code, your data, your accounts, documented for handover.
FAQ

Questions, answered

Can you work with our existing buying and reporting stack?
Yes. Builds are designed around the tools already in place — Mediaocean Prisma for trafficking, AgencyAnalytics, Tableau, or Looker Studio for reporting, Nielsen and Comscore data for planning — so planners keep working the way they work, with less manual transfer between systems.
How do you handle client reporting without adding headcount?
By automating the assembly, not the judgement. Platform data is pulled on a schedule, mapped once to each client's definitions, and rendered into the report format they already receive. Account managers review and annotate instead of rebuilding spreadsheets.
What about pacing and trafficking errors?
Pacing checks run against flight dates and budgets automatically, and trafficking changes go through a single checklist with an audit trail: who changed what, when, and for which insertion order. Errors get caught before they reach a live campaign.
Do we need a huge dataset to start?
No. Most useful first models train on data you already hold — a catalogue, a client list, a history of campaigns. The data audit at the start tells you honestly whether the first use case is viable before any build begins.
Who owns the model after launch?
You do. Models, training pipelines, and evaluation sets are documented and handed over, with a retraining schedule agreed up front so accuracy does not quietly decay.
Currently available for select engagements

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