Magna AI is a global integrated-value-chain AI transformation factory, architecting the future of the intelligent enterprise. Through a unified approach that spans strategy, engineering, integration, and operations, Magna AI delivers secure AI infrastructure, applications, and services designed to drive measurable, scalable, and organization-wide transformation. Powered by next-generation technology from Trend Micro, NVIDIA, and Wistron Digital Technology Holding Company, Magna AI enables enterprises to evolve into intelligent, adaptive, and future‑ready organizations confidently. Magna AI enables enterprises to evolve into intelligent, adaptive, and future‑ready organizations confidently. Building the enterprise AI economy.
Job Summary:
Own, build, and scale the AI Applications & Engineering Practice as a full‑lifecycle, production‑grade business unit. The role is accountable for end‑to‑end AI application realization, from business case and use case engineering through architecture, development, deployment, and ongoing operation of enterprise‑grade and sovereign AI applications.
This role governs what AI applications we build, how they are engineered, how they are secured, how they scale, and how they deliver measurable business outcomes.
Practice Scope:
A. AI Application Lifecycle Ownership
End‑To‑End Responsibility Across All Stages
1. Business Case & Use Case Engineering
Translate business objectives into AI‑applicable use cases • Identify where AI adds value vs traditional automation • Define measurable outcomes, KPIs, and value realization models • Build ROI, TCO, and adoption models • Prioritize use cases by feasibility, impact, and risk
2. Functional Scoping & Solution Design
Define functional requirements • Determine AI vs rules‑based vs hybrid approaches • Identify user journeys and interaction models • Decide build vs buy vs augment • Scope boundaries, integrations, and dependencies
3. AI Architecture & Engineering Design
Application Architecture: Modular and composable design • Event‑driven, agent‑based, service‑oriented architectures • Multi‑tenant vs single‑tenant • API‑first and integration‑first patterns
AI & Model Integration: Model selection (LLMs, vision, speech, predictive) • Fine‑tuning vs RAG vs orchestration • Multi‑model and multi‑agent architectures • Latency, cost, accuracy trade‑offs
Data Engineering: Ingestion and transformation pipelines • Feature stores and embeddings • Data quality, lineage, observability • Real‑time vs batch processing
Security & Governance by Design: Input/output validation • Prompt and model security • Access control and identity • Auditability, explainability, compliance
4. AI Application Development
Frontend AI‑driven UX • Backend services and orchestration • Agent logic, workflows, tool integration • Model serving and inference pipelines • Human‑in‑the‑loop workflows
Practices: Production‑grade standards • Versioning (models, prompts, agents) • CI/CD for AI applications (MLOps + AppOps)
Functional and non‑functional testing • Hallucination, bias, drift testing • Load, performance, resilience • Security and adversarial testing • Regulatory and policy validation
Deliverables: AI test frameworks • Risk and control matrices • Acceptance criteria
6. Deployment & Runtime Operations
Production Ownership
Deployment across cloud, hybrid, and on‑prem AI factories • Runtime monitoring and observability • Cost control and inference optimization • Model lifecycle management • Continuous improvement and retraining
User onboarding and enablement • Adoption tracking and behavior analysis • Continuous feedback loops • KPI tracking and value realization reporting
B. Application Domains Covered by the Practice
Enterprise AI (HR, Finance, Legal, Operations, Sales) • Industry AI (government, healthcare, energy, finance) • Agentic AI workflows • AI copilots and decision‑support systems • AI automation and orchestration platforms • Mission‑critical and regulated AI systems
Practice Management Responsibilities:
• Practice Portfolio & Offerings: AI use case discovery • AI application architecture • AI engineering and build • Agentic workflow development • AI application operations • Reference architectures and reusable assets
• Commercial & P&L Ownership: Revenue, margin, utilization, cost • Pricing for AI development, platforms, subscriptions, usage‑based services • Governance of large AI programs
• Delivery Governance & Quality: Engineering standards and guardrails • Consistency across applications • Escalation for high‑risk deployments • Contractual, regulatory, ethical compliance
• Platform, Tooling & Standards: Approved AI stacks and tools • Model, prompt, agent standards • Internal accelerators • Interoperability with infrastructure & cloud practices
• Executive & Stakeholder Engagement: Executive advisory • Business‑to‑AI roadmap translation • Representation in strategic programs and partnerships
Required Profile
12–15+ years in software engineering with deep AI exposure • Proven delivery of production AI applications • Strong LLM, agentic, and AI platform knowledge • Experience in regulated enterprise environments • Ownership of large engineering practices or platforms
