Role mandate
Embed inside Australian customer teams, turn ambiguous business problems into working systems running in production on AWS, and make Minfy’s first ANZ deployments referenceable.
About Minfy
Minfy is an applied AI and technology services company with deep AWS, cloud, data and engineering capability. As an AWS Premier Tier Services Partner and Managed Service Provider, Minfy supports customers from advisory and migration through modernisation, AI implementation and ongoing managed operations. Forward deployed engineering is how Minfy intends to prove that capability in Australia: engineers sitting with the customer, shipping working software early, then hardening it into something the customer can run.
The opportunity
A forward deployed engineer is measured by what runs in the customer’s environment, not by what is documented. The role sits with the customer’s engineers and business users, finds the problem behind the brief, builds a working version quickly and then makes it production-grade — with Minfy’s architects and delivery pods behind them. This suits an engineer who is energised rather than drained by ambiguity, customer contact and unfamiliar estates. The work spans applications, data pipelines, integrations and applied AI, and the same person is expected to build it, secure it, instrument it and hand it over cleanly.
Key responsibilities
· Embed with Australian customer teams to understand workflows, data, constraints and the outcome that actually matters to the business.
· Design and build working solutions on AWS — applications, data pipelines, integrations and AI or agentic systems — from prototype through to production.
· Get a first working version in front of real users quickly, then harden it: tests, infrastructure as code, CI/CD, observability, security controls and runbooks.
· Integrate with the customer’s existing estate — legacy systems, identity, data sources and networks — and be direct about what will not work.
· Instrument and evaluate AI features: retrieval quality, evaluation harnesses, guardrails, human-in-the- loop design and failure handling.
· Work with Minfy’s engineering pods to scale delivery once a pattern is proven, and hand over with documentation the receiving team can actually use.
· Support pre-sales technically through discovery workshops, proofs of value, demonstrations and the technical sections of proposals.
· Feed reusable patterns, accelerators and components back into Minfy’s engineering practice.
· Maintain professional customer communication on expectations, risks and trade-offs, delivering unwelcome news early rather than late.
Candidate profile
· Strong hands-on software or data engineering experience with genuine production ownership, and Australian work rights.
· Demonstrable AWS depth across compute, core networking, data services, IAM and cost management; certification is desirable, evidence is preferred.
· Experience shipping applied AI or GenAI features into production — retrieval, evaluation, guardrails, agentic workflows — or clear capability to do so.
· Evidence of working directly with non-engineering stakeholders and translating vague requirements into shipped software.
· A bias toward working software over documents, and comfort being the only engineer in the room.
· Sound judgement on security, privacy and data handling inside customer environments.
· Willingness to be based in Sydney, present on customer sites and to travel within Australia.
Profiles to avoid
· Engineers who have only worked behind a product manager or business analyst and have never faced a customer directly.
· Prototype-only builders who have never operated, monitored or supported what they shipped.
· Candidates whose AI experience is limited to demonstrations, notebooks or prompt experimentation.
· Candidates who need fully specified requirements, a mature platform and a stable backlog to be productive.
· Consultants whose contribution is assessment, advice and slideware rather than code.
First 90 days
Period Expected outcomes Complete technical onboarding and AWS environment access; ship one internal accelerator or Days 1–30 reference implementation; participate in two customer discovery sessions. Deploy onto the first customer engagement; deliver a working prototype validated by real users Days 31–60 within the first four weeks of that engagement. Take at least one customer solution into production or pre-production with tests, IaC, monitoring Days 61–90 and a documented handover; contribute one reusable pattern to the practice library.
Year 1 delivery and impact targets
Recommended planning targets, subject to final approval in the annual delivery plan. Definitions and treatment are set out in the common framework later in this pack.
Measure Year 1 target Notes
Customer solutions accepted and running in the Production deployments 4–6 customer’s production environment
Days on customer-funded engagements as a share of Deployment utilisation ≥70% available working days
From engagement start, on standard scopes, validated by Time to first working version ≤4 weeks real users
No critical defects escaping to production; handover Quality Zero critical escapes accepted first time in ≥80% of cases
Measure Year 1 target Notes
Patterns, accelerators or components adopted by another Reusable contribution ≥3 patterns engagement
Named reference or written endorsement from at least Customer endorsement 2 customers two Australian customers
Recruiter screening scorecard
Assessment area Points Evidence required
Has built, shipped and operated real systems; can explain their own Production engineering depth 30 code and design decisions
Practical depth across compute, data, IAM and cost; services actually AWS and cloud fluency 20 run in production
Has taken AI features to production with evaluation, guardrails and a Applied AI capability 20 view on failure modes
Works directly with customers under ambiguity and communicates Customer-facing composure 20 risk early
Ownership and pace 10 Ships, operates and follows through without supervision
Recommended shortlist threshold: 75/100, with no score below 3/5 for production engineering depth or customer-facing composure. All shortlisted candidates must complete the practical technical assessment described in the common framework.
Mandatory recruiter questions
· Tell me about something you built that is running in production today. What does it do, who uses it, and what would break it?
· Describe a time you were embedded with a customer and the real problem turned out to be different from the brief. What did you do?
· What have you put into production that uses a large language model? How did you evaluate it, and what guardrails did you build?
· Walk me through how you take a prototype to production — testing, infrastructure, monitoring, security and handover.
· When did you last tell a customer something they did not want to hear? How did you handle it?
· Which AWS services have you personally run in production, and what did they cost to operate?
Pay: $150,000.00 – $190,000.00 per year
Work Location: In person
