Consulting & AI engineering · Financial services

You have the mandate. You don't have the bench.

PAAR Systems brings senior consultants and AI engineers who understand both transformer architectures and a bank's control environment. We embed with your teams, take AI from strategy to production, and leave you able to run it.

Start a diagnostic sprint Explore our services

~25 yrsAustralian banking and program leadership

8Specialist roles, from architects to model risk analysts

6–14 wksTypical fixed-scope build to a deployed workflow

AU · INAustralian clients, India engineering base

Start a conversation

Start with a diagnostic sprint, or tell us the gap.

Two to three weeks to a scored use-case portfolio and one recommended first build. Or describe the role you're missing and we'll put named specialists in front of you.

Emailcontact@paarsystems.com

Webwww.paarsystems.com

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What we deliver

One team across the whole AI lifecycle.

Eight capabilities in four phases, delivered by the same senior team so nothing is lost between the slide and the system. Select a segment to explore.

Why banking AI stalls

Five places a pilot breaks on the road to production.

None of them are technical. All five are organisational, and all five are solvable if you plan for them before the first demo.

Demo lands

Model risk governance

No one owns validation and drift documentation.

Typically month 4

Data residency & egress

"Send it to our API" ends the conversation.

At architecture review

Third-party risk

Vendor onboarding runs its own queue.

Rarely in the timeline

No outcome owner

Innovation runs the pilot; the business owns the P&L.

At the handover

Undefined success

No baseline, so value can't be proven.

At renewal

In production

Not sure where AI fits yet?

A two to three week diagnostic sprint gives you a scored use-case portfolio and one recommended first build.

Who actually shows up

A small senior pod, built around one accountable lead.

The people in the pitch are the people doing the work, embedded in your tooling and handing capability to your own team.

Need a specific role filled?

Tell us the gap and we’ll put named senior specialists in front of you.

Engagement models

Buy the smallest thing that answers your question.

Typical length of each model, drawn to scale over twelve months. Select a model to see who shows up and what you end with.

Typical minimumTypical rangeRolling or ongoing

How an engagement runs

Four steps, each with a defined output.

FrameOne week

ProveTwo to six weeks

HardenControls & evidence

Hand overCapability stays

Define the problem in business terms and agree what success is worth in money or hours.

→ Measured baseline

Smallest credible build on real data, with an evaluation harness from day one.

→ Evidence vs baseline

Monitoring, failure modes, human-in-the-loop design, model risk and TPRM artefacts.

→ Approval-ready system

Runbooks, training and a named owner on your side. We stay only where you want us.

→ Your team runs it

Pick a starting point.

Most clients start with a diagnostic sprint and grow from there.

Regulation is a design input

Every obligation maps to a control we design in.

Retrofitting controls is how pilots die at month nine. This is how each obligation shapes what we build.

Primary controlSupporting control

Designing for CPS 230 and CPS 234?

We map every obligation to a control before the first build.

Accelerators we bring

We start from our own platform, not a blank page.

Five agents on one runtime and one data plane, deployed inside your tenant behind a human approval gate. Select an agent to see what it does.

YOUR TENANT · NO DATA EGRESS

People

Analysts, ops teams, contact-centre agents, scheduled triggers

Approval gate

A named human approves anything consequential

Agents

Runtime

Planner, allow-listed actions, memory, cost limits

Services

Retrieval, guardrails, evaluation, audit log, swappable models

Data plane

Documents, email, transcripts, core tables, lineage

Systems

Core banking, lending, CRM, warehouse, case & telephony

Platform layerIn build, design partners openPrototype

Become a design partner.

Passage and Redress are in build, with design-partner places open.

How we operate

Principles we will be held to.

Advice on AI in a bank only works if it is trusted. These are the lines we hold.

Selected experience

Already inside financial-services programs.

Client names are withheld. We are happy to talk through the work in a first conversation.

2markets sourcing Data & AI talent

Big Four bank · Data & AI

Snowflake-led data & AI transformation

Data scientists and AI engineers sourced across Sydney and India for a large transformation program.

3senior specialists embedded onshore

Financial services · Platform

Platform engineering pod

Platform Engineering Lead, SRE and DevOps Lead embedded in Sydney for a global IT services provider.

3global SIs supplied

Global SIs · Delivery

Blended delivery teams

Ongoing specialist supply to global system integrators on enterprise and financial-services programs.

Talk to the people who do the work.

The people in the first conversation are the people on the engagement.

Careers at PAAR Systems

Build AI that banks can actually approve.

We hire senior data and AI people who want to ship real systems inside regulated institutions, in small teams where your name is on the work. Roles are based in Sydney and across our India hubs.

Senior, small teams

No pyramid staffing. You scope the work and you do the work.

Regulated, real-world AI

Data platforms, models and agents that pass model risk and go live.

Sydney & India

Onshore client-facing roles, plus engineering hubs in Hyderabad, Bengaluru and Pune.

Permanent or contract

Most roles are open to either, with clear terms from day one.

Open roles

Current opportunities

Location Type

Don't see your role?

We're always keen to hear from senior data engineers, data scientists, ML and AI engineers, and banking domain experts.

hr@paarsystems.com