Jobs at Inviso Corporation

Own product direction for an enterprise AI platform, including requirements, roadmap, acceptance criteria, scope, and stage-gate processes. Translate customer and stakeholder needs into prioritized, testable requirements; manage trade-offs among value, velocity, leverage, and technical risk; and partner with engineering on delivery decisions. Recruit design partners, validate customer needs, document decisions, communicate with executives and clients, and maintain disciplined delivery across multiple product teams.

Lead governance, risk, and compliance activities for an enterprise AI platform. Define and mature SOC 2 and ISO 27001 controls, collect evidence, track remediation gaps, prepare audits, and create customer-facing security documentation. Partner with engineering, security, product, auditors, and client stakeholders to embed practical, automated compliance practices covering data handling, retention, access control, privacy, AI governance, and customer assurance.

Designs and secures enterprise AI platform foundations for mission-critical environments. Responsibilities include threat modeling, tenant isolation, identity and authorization architecture, secure CI/CD, compliance-by-design, and protection of systems executing untrusted code, tools, or agent actions. The role partners with client engineering, product, security, and business stakeholders to deliver secure, reliable, and compliant platform capabilities.

Design, build, operate, and support backend services powering an enterprise AI platform. Responsibilities include developing APIs, orchestration, entitlements, tenancy, metering, integrations, data access, and platform services. Own services across architecture, implementation, testing, deployment, monitoring, reliability, and support while applying distributed-systems principles. Collaborate with client product, engineering, data, and platform stakeholders in a remote consulting environment.

Design, build, and operate cloud infrastructure, deployment, observability, and reliability capabilities for an enterprise AI platform. Responsibilities include infrastructure-as-code, Kubernetes, CI/CD, incident response, SLOs, cost optimization, production readiness, automation, and support for hybrid, edge, on-premises, and customer-controlled environments. The role is client-facing, remote, and may involve travel.

Design, build, and operate cloud infrastructure and SRE capabilities for an enterprise AI platform. Responsibilities include infrastructure-as-code, landing zones, networking, Kubernetes, CI/CD, observability, incident response, SLOs, production readiness, automation, cost optimization, and support for hybrid, edge, air-gapped, and customer-controlled environments. The role is remote, client-facing, and requires strong collaboration and reliability ownership.

Build and operate production AI capabilities across enterprise platforms, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and cost optimization. Ship reliable, safe, measurable AI features; develop evaluation frameworks and quality controls; monitor production behavior; and collaborate with client-facing product, engineering, platform, backend, and security teams to deliver practical AI solutions.

Build and operate production AI capabilities, including agent runtimes, model routing, RAG, tool calling, guardrails, evaluation, monitoring, and quality controls. Ship reliable, measurable, safe, and cost-aware AI features, improve systems using production evidence, and collaborate with backend, platform, product, security, and client teams in a consulting environment.

Owns the roadmap, product requirements, scope, acceptance criteria, and stage-gate processes for enterprise AI platform capabilities. Partners with customers, executives, engineers, product leaders, and design partners to translate needs into prioritized decisions and delivery plans. Manages trade-offs among customer value, technical risk, platform leverage, and product velocity while validating solutions and maintaining delivery discipline in a client-facing consulting environment.

Designs, builds, operates, and supports backend services powering an enterprise AI platform. Responsibilities include developing APIs, AI gateway and orchestration capabilities, entitlements, tenancy models, data access patterns, integrations, and platform services. The engineer owns services throughout design, implementation, testing, deployment, monitoring, reliability, and support while collaborating with client product, engineering, data, and platform stakeholders.

Build and operate production AI systems, including agent runtimes, RAG, model routing, tool calling, guardrails, evaluation, monitoring, and optimization. Ship reliable, safe, measurable, and cost-aware AI capabilities while improving quality through evidence-based monitoring. Collaborate with backend, platform, product, security, and client teams in a consulting environment.

Design and secure enterprise AI platform foundations, focusing on threat modeling, tenant isolation, identity, authorization, secure CI/CD, compliance-by-design, and protection of systems executing untrusted code or agent actions. Partner with client engineering, product, security, and business stakeholders to make pragmatic architecture decisions and enable reliable, compliant delivery.

Build and maintain full-stack customer-facing and administrative experiences for an enterprise AI platform. Develop front-end interfaces, APIs, backend integrations, and supporting services for configuration and operational workflows. Own features from customer problem discovery through production delivery, collaborating with product, design, platform engineering, and clients. Ensure usability, accessibility, performance, reliability, automated test coverage, and maintainability in a remote consulting environment.

Build and maintain customer-facing and administrative experiences for an enterprise AI platform. Develop front-end interfaces, APIs, backend integrations, and supporting services across the full stack. Own features from customer problem discovery through production delivery while partnering with product, design, platform engineering, and clients. Ensure usability, accessibility, performance, automated test coverage, reliability, and maintainability in a remote, client-facing consulting environment.

Drive Microsoft 365 Copilot trial adoption across ASEAN by enabling CSP partners and resellers, supporting customer qualification and activation, resolving adoption blockers, managing pipelines, tracking KPIs, and supporting trial-to-paid conversion. The role also coordinates with Microsoft teams and distributors, maintains CRM accuracy, delivers performance insights, and supports continuous process improvement.

Lead end-to-end Microsoft 365 architecture engagements, including discovery, design, deployment, governance, security, compliance, adoption, and AI readiness. Serve as the primary technical advisor for enterprise and mid-market customers, guide delivery teams, manage rollout sequencing and change programs, and support presales proposals and statements of work. Configure solutions across identity, device management, collaboration, data governance, and compliance while driving customer outcomes, satisfaction, utilization, and revenue targets.

Designs, implements, migrates, and supports secure Microsoft 365, Azure, and modern workplace environments. Leads client-facing technical engagements, translates business needs into solutions, manages project delivery, automates administration, and governs identity, security, compliance, and collaboration platforms. The role also documents architectures, resolves incidents, supports integrations, mentors team members, and identifies opportunities to improve client productivity while maintaining high billable utilization.