Most enterprises are not struggling with AI because the models do not work. They are struggling to turn frontier capability into safe, adopted systems that materially change how work gets done.
monō closes that gap. We combine an enterprise AI orchestration platform with small, senior pods of engineers and operators who embed inside our clients’ businesses. We build on client infrastructure, work with the systems they already run and prove value through working capability, not roadmaps.
We are looking for a Forward Deployed Engineer (FDE) to own technical outcomes from discovery through production and adoption.
As a Forward Deployed Engineer at monō, you will work directly with client operators, engineers and leaders to turn ambiguous operational problems into safe, measurable and adopted production systems.
You will uncover how the work actually happens, identify the narrowest valuable intervention and build the solution yourself. You will define how performance is evaluated, establish the path to production, support adoption and turn what you learn in the field into reusable platform capability.
This is a hands-on engineering role. You will write and ship code, debug integrations, design evaluations and operate what you build. You will also work closely with frontline users, security teams and executives. On any given engagement, the highest-leverage action may be tracing a failing dependency, narrowing the scope, redesigning an evaluation set, responding to an incident or explaining why a requested launch is not yet safe.
You will have meaningful autonomy and clear accountability for the result.
- Observe and map the real workflow: users, decisions, systems, hand-offs, exceptions, incentives and controls.
- Establish the baseline, decision owner and intended outcome before selecting technology.
- Distinguish the stated request from the underlying operating problem.
- Turn ambiguity into a testable problem definition, explicit assumptions and a focused first release.
- Design and implement full-stack AI systems spanning data, APIs, models, agents, interfaces and enterprise integrations.
- Make deliberate choices between deterministic software and probabilistic model behaviour.
- Deliver inside imperfect environments with fragmented data, legacy systems, limited client capacity and shifting dependencies.
- Define what “good” means with users and domain experts.
- Build evaluation datasets, failure taxonomies, acceptance thresholds and regression tests.
- Measure quality, grounding, latency, cost and workflow impact—not just whether a demonstration looks compelling.
- Make material outputs traceable, represent uncertainty honestly and preserve human decision rights where consequences matter.
- Design for client requirements across identity, access, privacy, data residency and auditability.
- Build least-privilege integrations, observability, safe fallbacks, release gates and rollback paths.
- Anticipate model, data, dependency and organisational failure—and ensure the system degrades safely.
- Stay accountable after launch: monitor behaviour, respond to incidents, communicate risk early and turn failures into stronger controls.
- Build with the people who will use and operate the system, not around them.
- Instrument adoption and changed user behaviour alongside technical performance.
- Connect delivery to defensible measures such as cycle time, capacity, quality, risk, revenue or experience.
- Identify the next value opportunity only after the first has earned trust and produced evidence.
- Separate client-specific policy and configuration from reusable primitives.
- Turn field learning into platform improvements, connectors, evaluation harnesses, reference architectures and playbooks.
- Leave clients better able to operate, evaluate, troubleshoot and extend the system themselves.
On a strong engagement, you will:
- align the client around a specific user, workflow, baseline, owner and measurable outcome;
- demonstrate a credible working slice within a fixed time horizon
- move towards a controlled production release within weeks, supported by evaluation evidence and explicit safety gates;
- ship useful improvements frequently while protecting quality and client trust;
- achieve sustained adoption and a defensible business result—not merely complete a build;
- leave behind an auditable, operable system and a client team equipped to run it; and
- create at least one reusable improvement that makes the next monō deployment faster or safer.
Good judgement may mean narrowing, delaying or stopping a release. We value working evidence and clear decisions over theatre or volume.
What you bringWe care more about demonstrated capability than a particular title, degree or career path. You should be able to show that you have:
- Built production software personally. You can move from architecture into code, debug unfamiliar systems methodically and explain what you built. You are comfortable across backend, data and a practical user interface.
- Strong software fundamentals. Production fluency in Python and TypeScript/JavaScript, or comparable languages, with strong SQL, API, data-modelling, testing and system-design skills.
- Shipped applied-AI systems. You have worked hands-on with model APIs, retrieval and context, structured outputs, tool use or agents, evaluation and production monitoring. You understand that the model is one component of a larger system.
- Owned production and security trade-offs. You have practical experience with cloud infrastructure, deployment pipelines, identity and access, secrets, privacy boundaries, observability, resilience and incident response.
- Worked directly with customers or internal operators. You can run discovery, translate between domain and technical teams, challenge a stakeholder respectfully and stay clear under pressure.
- Taken work from ambiguity to adoption. You have owned more than a prototype or hand-off and can point to changed user behaviour or a measurable operating result.
- Operated with high agency. You find the constraint, sequence the work, ask for help intelligently and do not wait for a complete brief.
- Used AI tools accountably. You use them to improve speed and quality while remaining able to explain, test and modify every material output.
The strongest evidence is a system you personally helped take from discovery into production—including what failed, what changed for users and what became reusable.
Additional helpful experience- Experience delivering in financial services, healthcare, government or another regulated or security-sensitive environment.
- Integration experience with enterprise systems such as CRM, ERP, document management, contact-centre, identity or data platforms.
- Experience as an early engineer, technical founder, field engineer, customer-facing platform engineer or hands-on technical consultant.
- Designing human-review workflows for consequential decisions.
- Building evaluation, agent-operations, data-lineage or AI-governance tooling.
- Turning repeated customer needs into durable product capability.
No single background is preferred. We are building a team with different strengths, but every FDE must meet a high bar for hands-on engineering, production ownership and independent client judgement.
This role is not- a pre-sales role that ends when a solution is scoped;
- an architecture or advisory role where another team does the build;
- a conventional implementation role measured by configuration or ticket completion;
- a core-platform role with limited ownership of the client outcome; or
- a mandate to maximise autonomy regardless of risk.
You may help shape an expansion opportunity, but your primary accountability is the technical outcome: delivery, production readiness, adoption and learning that compounds into the platform.
This role is based in Sydney. Regular in-person work at monō’s Martin Place office and client sites is an inherent part of forward deployment, and client-site travel may be required. We will discuss the expected cadence and any reasonable workplace adjustments early in the process.
This is not a remote-only role.
