AI Technical Architect

Job Description: AI Technical Architect
Location: India (Hybrid, supporting a US-based organization)
Reports to: Sr Director BTO, United States
Function: Business Technology Office (BTO) — AI Enablement
This engagement supports US-based business divisions, so significant real-time collaboration with US-based teams (including Security, Compliance, IT, and Governance) is expected. As an independent contractor, you retain flexibility in how you structure your day: you are expected to maintain a daily core-hours overlap window with US Eastern/Pacific business hours (exact window to be agreed) to support architecture reviews, working sessions, and cross-team alignment meetings, while managing your remaining hours and schedule independently to meet agreed deliverables and milestones. This is a deliverable- and availability-based engagement, not a fixed-shift schedule.
Role Summary
The AI Technical Architect owns the engineering, security, governance, and reusability of AI solutions across the company. This person takes rapidly prototyped (“vibe coded”) AI agents, bots, and apps — often produced by the AI Business Architect or business teams — and makes the disposition call (retain, refactor, wrap, replace, or rebuild), then re-architects and rebuilds them into secure, production-grade, scalable solutions, reusing existing prototype code only where company policy and engineering standards allow. The role works directly with Security, Compliance, IT, and Governance teams to stay current on requirements and embed them into architecture from the start (not just at review time), builds highly reusable AI solutions within the company’s AI Registry, and drives architectural discussions, alignment, gap analysis, and roadmap planning with those teams.
This is a hands-on architecture role — not a strategy-only enterprise architect position. Depth in building, securing, and operating production AI/agentic systems is expected.
NetApp’s AI strategy is delivered through a portfolio of enterprise AI/agent platforms. This role will take on technical administration and security-rollout ownership for one of these platforms, partnering with NetApp Security on connector approvals and owning platform configuration and feature delivery.
Key Responsibilities
Take ownership of prototype/“vibe coded” AI solutions and make the disposition decision: retain, refactor, wrap, replace, or rebuild — separating disposable experimentation from production-grade code while preserving validated learning.
Design and build production-grade LLM, RAG, and agentic applications, including multi-agent orchestration and tool/API integration, following company engineering and security standards.
Harden, secure, and scale-test AI solutions for production: address latency, throughput, reliability, cost, data privacy, access control, and agent behavior risk.
Own and grow the company’s AI Registry of reusable assets: agent templates, prompt and RAG patterns, approved integrations, evaluation suites, security controls, and deployment playbooks.
Partner directly with Security, Compliance, IT, and Governance teams — proactively, during design, not only at review — to stay current on requirements and ensure solutions meet them by construction.
Perform gap analysis on current AI architecture/practices and convert findings into a prioritized technical roadmap aligned with enterprise architecture direction.
Communicate technical trade-offs, risks, and rebuild/reuse decisions clearly to both technical and business stakeholders, including the AI Business Architect.
Mentor and set quality bars for engineers/contributors producing AI solutions, including secure agentic coding patterns.
Own technical administration of the platform: user provisioning/deprovisioning, environment configuration, and integration health monitoring.
Partner with NetApp Security to evaluate, approve, and roll out new connectors/integrations on the platform.
Implement and configure new platform features/capabilities as they are released by the vendor.
Lead technical knowledge transfer from the outgoing support team and stabilize any broken processes (onboarding, distribution lists, access management) inherited during transition.
Required Skills & Experience (Must-Have)
Deep understanding of building AI agents, bots, and applications, including GenAI/LLMs, RAG, and agentic architectures/multi-agent orchestration.
Demonstrated ability to rapidly prototype (“vibe code”) with AI coding assistants/LLMs, AND to convert such prototypes into secure, production-grade code.
Experience embedding responsible AI, security, governance, auditability, and compliance requirements directly into architecture and delivery.
Demonstrated experience building reusable architecture assets, reference patterns, or platform accelerators (equivalent to an internal “AI Registry”).
Ability to communicate technical trade-offs and decisions clearly to both technical and non-technical stakeholders.
Explicit judgment and a working framework for prototype disposition (retain/refactor/wrap/replace/rebuild) and reuse-vs-rebuild trade-offs under real policy constraints.
Hands-on proficiency with modern AI coding/agent ecosystems — some or all of: OpenAI ecosystem, Anthropic ecosystem, Google Gemini ecosystem, Snowflake CortexAI, TrueFoundry, Abacus.ai, Cursor, Microsoft AI ecosystem (Copilot/Azure AI), and/or similar AI ecosystems. Breadth across multiple ecosystems is expected; depth in every single one is not required, but familiarity with none of them is not acceptable for this role.
Proven experience as a technical administrator/owner of an enterprise AI agent or orchestration platform — e.g., OpenAI, Anthropic, Google Gemini Enterprise, Snowflake CortexAI, TrueFoundry, Abacus.ai, Microsoft AI ecosystem, and/or similar — including provisioning, integration/connector rollout, and platform configuration at company scale.
Preferred Skills & Experience (Nice-to-Have)
Experience building internal AI enablement platforms or reusable accelerators at enterprise scale.
Proven experience hardening, securing, and scale-testing AI/software solutions for production (security reviews, load/performance testing, reliability engineering).
Knowledge graphs, RDF/SPARQL, or advanced semantic/data architectures.
Hybrid cloud/on-premises/edge deployment and legacy system integration experience.
Experience mentoring engineers on secure agentic coding patterns and engineering quality gates.
Strong evaluation and auditability tooling expertise for AI systems.
Success Metrics (Indicative)
Number and adoption rate of reusable AI Registry assets produced.
Production readiness/security incident rate of AI solutions shipped.
Cycle time from prototype hand-off to secure production deployment.
Quality and timeliness of architecture governance artifacts (gap analyses, roadmaps, exception documentation).
Cross-team alignment outcomes with Security, Compliance, IT, and Governance (e.g., reduced rework, faster sign-off).
Working Relationship with the AI Business Architect
This role partners closely with the AI Business Architect. The AI Technical Architect owns how the solution is engineered, governed, secured, reused, and operated at scale; the AI Business Architect owns why, where, for whom, and how adoption succeeds. Architecture approval, prototype disposition, production-readiness criteria, and technical roadmap decisions rest with this role.
Key skills

Work arrangement
Hybrid