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Future Secure AI - Site Reliability Engineer - Recruiter Screen Prep
Position: Site Reliability Engineer
Company: Future Secure AI
Location: Remote/Hybrid in Australia
Interview Type: First round - Recruiter screen (light & general questions)
🎯 Role Overview
Future Secure AI delivers secure "digital workers" (AI agents) into enterprise environments. As an SRE, you'll build and run the guardrails that keep these AI services reliable, compliant, and auditable across AWS and Azure. Expect heavy interaction with Kubernetes, GitOps (Argo CD), Terraform, observability, and secure CI/CD for AI workloads that touch customer data.
Key Focus Areas:
- Kubernetes platform reliability (EKS/AKS) with GitOps (Argo CD)
- Infrastructure as Code with Terraform and policy-as-code
- Secure supply chain from Git → CI → CD (signing, SBOMs, scans)
- Observability with cost control and actionable SLOs/runbooks
- Cross-cloud patterns (AWS primary, Azure present) and automation (Python/Node)
- Incident readiness and compliance for AI/PII-sensitive workloads
🏢 About Future Secure AI
Intel: Limited public info (LinkedIn). Company builds AI "digital workers" deployed securely on customer networks; 201–500 employees. Treat signals as constrained and use recruiter to clarify specifics.
Culture Keywords (inferred from domain): Security-first, reliability/trust, customer-centric, compliance-minded, ownership.
Tech Environment (from JD blurb): AWS + Azure, Kubernetes, Argo CD/GitOps, Terraform, CI/CD, Python/Node, Git.
Note: No external interview Q reuse visible—lean on your platform/SRE wins and ask clarifiers early.
📊 Your Experience Match
✅ Strong Alignments
Cloud Infrastructure & Kubernetes (Domain - 2 years)
- Led AWS ECS → Kubernetes migration for 100+ microservices; platform uptime 99.9%.
- Built Argo CD GitOps, SLO Helm charts, secure pipelines; enabled 20+ teams to self-serve deployments.
Developer Experience & Platform Engineering (Viator - Recent / Domain)
- Cross-domain platform enablement (Backstage metrics, AI Slack bot, GitLab token automation).
- Built golden paths and standards that increased velocity while keeping guardrails intact.
Observability & Monitoring (Envato / Domain)
- New Relic → Datadog migration cut costs 45%; integrated PagerDuty workflows, improved incident resolution time ~40%.
- OTel instrumentation; ELK + SLO dashboards wired to runbooks for fast triage.
CI/CD Automation (illion / Domain / Viator)
- Lead time 2h → 10m (illion) via automated CI/CD; GitOps rollout for 15+ teams (Domain).
- Experience with GitHub Actions, Jenkins/Buildkite, Argo CD; secure-by-default pipelines.
Infrastructure as Code (Avinet / Domain)
- Terraform migrations for AWS with audit/compliance goals; reusable modules and drift detection.
- AWS CDK familiarity; treats infra like code with reviews, tests, and rollout discipline.
🔄 Transferable Skills
- Python/Node automation: Bots and tooling with retries/logging/feature flags.
- Security & Compliance: Led SOC2/ISO audits (illion); aligns to AI/data guardrails.
- Incident Management: MTTR reductions (Avinet); runbooks, SLO-driven alerts, and post-incident hardening.
📈 Areas to Develop
- Azure depth: Primary AWS background; offer a 30-60-90 ramp (pairing with Azure SME, lab mirroring prod, start with one service, map LB/DNS/identity/secrets, validate IaC/policies early).
- Company-specific AI architecture: Ask for deployment model, data boundaries, and SRE ownership.
Vision
Empowering teams to ship secure and reliable AI services with confidence.
Mission
Continuously improve developer and operator experience across clouds by providing guardrailed platforms, observability, and automation that keep AI workloads safe, compliant, and fast to deliver.
Goals, Challenges, Current State, Tech Vision, Initiatives, Quarterly Roadmap
💬 Recruiter Screen Questions & Talking Points
1. "Tell me about yourself / Walk me through your resume"
Your Answer (2-3 minutes):
“I’m a Senior Platform Engineer who specializes in platform reliability and scalability in regulated environments. Over the last 13 years, I’ve moved from building bare-metalservers to delivering platforms, championing modern SRE culture, and empowering teams through autonomy, observability, and strong engineering practices.
My core focus is to enable engineering organizations to ship high-quality features safely, quickly, and confidently:
- At Viator, I concentrated on Developer Experience, contributing to the 'Golden Paths' that standardized how we delivered software.
- At Domain, I led the strategy to migrate more than 100 services to a self-serve Kubernetes platform, achieving 99.9% uptime and zero-downtime migrations.
- At Envato, I treated observability as a product, migrating from New Relic to Datadog, cutting costs by 45% while improving incident response times through better signal-to-noise ratios.
I’m drawn to Future Secure AI because the challenge of deploying 'digital workers' securely is a perfect intersection of my background: it requires deep reliability engineering, strict security guardrails, and a customer-first mindset.”
2. "Why are you interested in Future Secure AI / this role?"
Your Answer:
“Three strategic reasons:
- The Mission Criticality: Deploying AI 'digital workers' isn't just a feature; it's a trust challenge. I want to build the reliability architecture that makes enterprise customers trust AI with their data.
- The Platform Engineering Fit: You need secure supply chains, GitOps at scale, and multi-cloud governance. These are the exact systems I've architected at Domain and Envato.
- The Cross-Cloud Complexity: I thrive from complexity to simplicity. The challenge of abstracting reliability patterns across multi cloud is the kind of 'hard problem' I want to solve next in a simple way.”
3. "What are you looking for in your next role?"
Your Answer:
“I’m looking for a role where I can be a Force Multiplier:
- Strategic Impact: I want to own the roadmap for a product that matters. I want to measure my success by influence and impact.
- Technical Complexity: I want to solve hard distributed systems problems—specifically around multi-cloud orchestration and securing AI workloads.
- Culture of Excellence: I want to work with a team that values 'production readiness' as a feature, where I can mentor others and raise the bar on engineering standards.”
4. "Why are you leaving Viator / looking for a new opportunity?"
Your Answer (Keep positive and forward-looking):
“Viator recently restructured its Australian engineering presence. While I’m proud of the Developer Experience platform we built there, I see this as an opportunity to pivot back to a mission-critical SRE role. I want to be closer to solving high-stakes reliability and security challenges for AI, extend my knowledge of multi-cloud orchestration, and build guardrails for AI workloads.”
5. "What's your experience with Kubernetes / Cloud platforms?"
Your Answer:
“I view Kubernetes as the platform of platforms. At Domain, I architected a multi-tenant platform that supported 100+ microservices with zero downtime.
- Strategy: I led the migration from ECS to EKS to unlock bin-packing cost savings and GitOps velocity.
- Cloud: I have deep architectural expertise in AWS (EKS, Lambda, Networking). For Azure, I treat it as a mapping exercise—translating my proven AWS patterns (e.g., IAM roles to Managed Identities) to the Azure ecosystem. I focus on the principles of reliability, which are cloud-agnostic.”
6. "Tell me about a time you had to solve a complex infrastructure problem"
Situation: At Domain, our architecture for 100+ services was inefficient and risky. We were running a separate ALB and ECS cluster for every service, which drove up costs significantly. Operationally, deployments were coupled to the CI pipeline—meaning a rollback required a full build re-run (taking 20+ mins)—and services were communicating via public endpoints, creating a security surface we needed to close.
Task: I saw the opportunity and led the strategy to consolidate onto a shared Kubernetes platform. My goals were to reduce infrastructure costs, enable instant GitOps rollbacks (decoupling deployment from release), and build secure internal routing.
Action:
- Strategic Architecture: I designed a multi-tenant K8s platform to replace the "cluster-per-service" model. Recognizing that density would increase, I proactively re-architected the VPC network (moving from /16 to /8 with /20 subnets) to prevent IP exhaustion before it happened.
- Modern Delivery: I selected Argo CD to decouple deployment from release. This shifted us to a GitOps model where rollbacks became instant configuration reverts rather than slow build processes.
- Execution: I led the migration squad to implement internal routing via K8s Gateway API and used a progressive pattern with weighted DNS to migrate services with zero downtime.
Result: We consolidated infrastructure (reducing ALB costs), achieved zero customer incidents during migration, and platform uptime hit 99.9%. The move to GitOps empowered 20+ teams to self-deploy safely, and we successfully enabled service-to-service traffic behind the firewall.
7. "What's your experience with observability and monitoring?"
Your Answer:
“I treat observability as a product. At Envato, our monitoring spend was spiraling, but we had low actionable signal. I led a strategic migration from New Relic to Datadog to redefine our alerting strategy around SLOs. This reduced alert fatigue, cut costs by 45%, and gave teams the confidence to own their own reliability. I focus on 'Mean Time to Understanding'—ensuring that when an alert fires, the runbook is linked and the root cause is visible via dashboards.”
8. "How do you approach CI/CD and automation?"
Your Answer:
"I build Secure Supply Chains. At Domain, I realized that 'speed' and 'security' were seen as trade-offs. I flipped that by architecting a 'Golden Path' CI/CD framework.
- Strategy: I embedded security gates (signing, SBOMs, Wiz scans) invisibly into the standard pipeline templates.
- Force Multiplication: This meant that the 'easiest' way to deploy was also the 'secure' way. Teams using my templates got automatic compliance and a 10-minute deploy time, while ad-hoc builds faced manual review friction. This drove 90% voluntary adoption."
9. "Walk me through a time you improved developer velocity"
Your Answer (STAR format - Principal Level):
Situation: At Domain, our "velocity" was capped by our tooling. We had 20+ product teams, but deployments took 45 minutes on average. Worse, the process was brittle—deployments were tightly coupled to the build pipeline, meaning a simple config change required a full rebuild. This friction meant teams deployed only twice a week, leading to large, risky change sets.
Task: I set a strategic goal to reduce deployment lead time to under 15 minutes and shift the organization to a "deploy-on-demand" culture. I wanted to build a system where the "right way" was also the "fastest way."
Action:
- Decoupling Strategy: I architected a move to GitOps (Argo CD), decoupling the "build" (CI) from the "deploy" (CD). This allowed instant rollbacks and configuration updates without rebuilding artifacts.
- The "Golden Path": I led a squad to build standardized Helm charts that abstracted away the complexity of K8s (ingress, sidecars, HPA). This meant a developer could spin up a production-ready service in minutes, not days.
- Embedded Guardrails: To remove manual security gates, I integrated SonarQube and Wiz directly into the pipeline. Security became a non-blocking automated check rather than a manual review.
Result:
- Velocity: Deployment frequency jumped from 2/week to 5/day per team.
- Speed: Lead time dropped from 45m to 9 minutes.
- Culture: 20+ teams moved to full self-service, freeing up the platform team to focus on reliability engineering rather than ticket-taking.
10. "Do you have experience with Infrastructure as Code?"
Your Answer:
“I build the governance layer for infrastructure. At Domain, I architected a modular library that enforced policy-as-code (e.g., encryption, tagging) by default. This allowed us to democratize infrastructure changes—teams could self-serve resources knowing they were compliant, while drift detection kept our audit posture clean. It’s about enabling velocity with guardrails, not gates.”
11. "What interests you about working remotely in Australia for this role?"
Your Answer:
“I operate with an 'Async-First' mindset. In my experience, distributed teams force better engineering discipline—decisions must be written down (RFCs), and systems must be self-documenting. I thrive in this environment because it rewards clarity and autonomy. Being based in Adelaide allows me to have deep focus time while overlapping sufficiently with the team for high-value syncs.”
12. "What are your salary expectations?"
Your Answer (Research-backed, flexible):
“I’m targeting a package around A$170k base + super, which reflects the strategic value I bring as a Principal-level engineer who can drive reliability and velocity at scale. However, the mission and the technical challenge are my primary drivers, so I'm open to discussing the total compensation structure.”
13. "Do you have Azure experience?"
Your Answer:
“My expertise is in cloud-native patterns, which transfer. While my deep production hours are in AWS, I approach Azure strategically. My 30-60-90 plan is to map our proven AWS patterns (GitOps, Secret Management, Ingress) to their Azure equivalents (AKS, Key Vault). I’ll pair with an SME to validate the nuances, but I expect to be shipping value within the first month by focusing on the architectural similarities rather than syntax differences.”
14. "What questions do you have for me?"
Your Questions (Strategic & High-Signal):
"As we scale these 'digital workers', what is the single biggest architectural bottleneck or reliability risk you foresee in the next 12 months?"
- Why ask: Shows you are thinking ahead about scale and risk, not just current state.
"How does the organization currently balance feature velocity against reliability and compliance? Is there a shared 'error budget' culture, or is SRE viewed as the 'gatekeeper'?"
- Why ask: Probes the engineering culture and maturity. Principal Engineers need to know if they are partners or blockers.
"Regarding the cross-cloud requirement: Are we aiming for full active-active portability (high complexity) or just portability of patterns and tooling? What is the business driver for that decision?"
- Why ask: Demonstrates you understand the massive cost/complexity trade-offs of multi-cloud and care about the business reason.
"What does the 'Golden Path' for a new AI service look like today? How long does it take a developer to go from 'hello world' to production with all guardrails in place?"
- Why ask: Focuses on Developer Experience and Force Multiplication.
"How is the SRE team positioned? Are we embedded with product teams to drive architecture, or are we a central platform team providing services?"
- Why ask: Clarifies your leverage and how you will influence the organization.
"What does success look like in the first 6 months for someone in this role?"
- Shows goal-orientation and desire to deliver value
"What has kept you here as long as you have been? Or what do you love about working here?"
"Is there anything you are looking for in the ideal candidate that you have not yet heard from me today?"
15. "Tell me a story about how you use Terraform"
Your Answer (STAR format):
Situation: At Domain, multiple AWS accounts had hand-crafted stacks that drifted over time, creating audit risk and slowing teams that needed infrastructure changes.
Task: Lead a push to standardize infrastructure as code so teams could provision safely without tickets while keeping compliance clean.
Action: Built a Terraform module library (networking, IAM, data stores) with policy-as-code defaults (tagging, encryption, limits), wired plan/apply pipelines with mandatory reviews and drift detection, and published a paved-path onboarding guide so teams could self-serve confidently.
Result: Teams moved off ad-hoc stacks to governed modules, audits stayed clean, and new environments shipped predictably without waiting on the platform team.
🎯 Key Talking Points to Emphasize
- Mission alignment: Reliability and security for AI “digital workers” where trust is critical.
- Production Kubernetes & GitOps: 100+ services, Argo CD, 99.9% uptime, SLO Helm charts.
- Observability with cost discipline: Datadog migration cut 45% costs; OTel/ELK; SLO-first alerts/runbooks.
- CI/CD & supply chain: Lead time 2h → 10m; signing/SBOM/scans baked into pipelines.
- IaC: Terraform modules with auditability; policy-as-code and drift detection.
- Autonomy & ownership: Cross-team enablement, golden paths, measurable velocity gains.
- Azure ramp plan: Explicit 30-60-90 with AWS→Azure pattern mapping.
⚠️ Potential Concerns & How to Address
Concern: "You don’t have deep Azure experience"
Response: "True—my production depth is in AWS. The reliability/GitOps/IaC patterns transfer. I have a 30-60-90 Azure ramp: pair with an SME, lab mirroring prod, start with one service, map LB/DNS/identity/secrets, validate IaC/policies early."
Concern: "Company specifics aren’t clear"
Response: "Public intel is limited. I’d like to learn your AI deployment model, data boundaries, and SRE ownership so I can tailor guardrails and observability quickly."
Concern: "Go experience"
Response: "My automation is primarily Python/Node/TypeScript/bash. I’m comfortable picking up Go for SRE tooling—have ramped on new languages quickly to meet platform needs."
📝 Additional Notes
- Tone: Confident, collaborative, mission-focused (Australian professional style)
- Keep it conversational: Recruiter screen, not deep-dive
- Be authentic: Ground every answer in real resume experiences
- Show enthusiasm: Reliability/security for AI is meaningful work
- Ask questions: Use recruiter to clarify architecture, ownership, and priorities
✅ Pre-Interview Checklist
- [ ] Review this prep guide the morning of the interview
- [ ] Review your resume—be ready to talk about any bullet point
- [ ] Skim Future Secure AI’s LinkedIn for recent updates (company size, roles, focus)
- [ ] Prepare your workspace (quiet environment, good internet, headphones)
- [ ] Have questions ready to ask
- [ ] Test video/audio if virtual interview
- [ ] Be ready 5 minutes early
Good luck, Lucas! Lead with reliability, security, and the GitOps/observability wins that keep AI services safe and always-on. 🚀