Architecture Governance & Cross-Pod Coordination Lead

HCL Technologies Limited; Wisconsin; Sur place; USD 180 000 - 250 000; Plein temps

Résumé du poste

HCL Technologies Limited seeks an experienced Enterprise AI Architect to own the reference architecture for AI, data, and Physical AI across edge, plant, and cloud. You will set standards, maintain ADRs, and guide Pods to reuse shared capabilities while driving governance across the organization.

You will lead strategic build-vs-buy-vs-partner decisions for GPU, lakehouse, and MLOps tooling, ensuring scalable, secure, and production-ready solutions aligned with business goals.

Qualifications

• 15+ years in enterprise / solution architecture, platform engineering, or industrial technology leadership.
• Demonstrated ownership of enterprise-scale architectures spanning data platforms, ML / AI, and OT / IT integration.

Responsabilités

• Own and evolve the enterprise target-state reference architecture for AI, data, and Physical AI across edge, plant, and cloud.
• Define enterprise standards for data platforms, model serving, retrieval, agent orchestration, integration, and observability.
• Maintain ADRs and reference blueprints used by Pods, including AI, energy/yield optimization, and digital twin.
• Lead build-vs-buy-vs-partner decisions for GPU stacks, data lakehouse, MLOps/LLMOps tooling, and Physical AI platforms.
• Coordinate delivery Pod portfolios, dependencies, roadmaps, and risk; drive reuse through a shared catalog.
• Establish intake-to-production pathways with architecture reviews and production readiness criteria.
• Partner with Responsible AI to embed governance gates in the delivery pathway.
• Define AI-supporting data architectures and standards for MLOps/AgentOps.
• Own OT/IT convergence architecture, edge-to-cloud data flows, and reference Physical AI architectures.
• Chair Architecture Review Board and report health and reuse metrics to leadership.
• Mentor Pod architects to raise enterprise architectural maturity.

Description du poste

The Office of AI has been established to lead the enterprise-wide adoption of artificial intelligence, advanced analytics, industrial automation, and Physical AI across the company's steel, wire, and casting operations. The Office runs a "Core & Flex" operating model: a central Core team sets the shared platforms, standards, and guardrails, while domain-aligned delivery Pods build and scale use cases close to the business. This role sits on the Core team and is a voting member of the AI Steering Committee — the cross-functional body, including the business-unit Presidents and enterprise leaders in Finance, HR, Operations, and Strategy, that sets AI priorities, approves investment, and governs risk. This is a hands‑on leadership role for a proven operator, not a purely advisory seat.

Role Mission

You own the enterprise AI reference architecture and the technical operating model that keep every AI, data, agentic, and Physical AI initiative coherent, reusable, secure, and scalable across all delivery Pods and all three business units. You are the single point of architectural accountability that prevents fragmentation, eliminates duplicated builds, and ensures that what one Pod proves becomes a shared capability the whole enterprise can reuse.

In the Core & Flex model, you are the guardian of the Core — the shared platform, standards, and guardrails — while enabling Pods to move fast within them. Success is measured not by how much technology is built, but by how much reliable value reaches production and how well it compounds over time.

Key Responsibilities

• Own and continuously evolve the enterprise target-state reference architecture for AI, data, and Physical AI spanning edge, plant, and cloud, and ensure every Pod solution maps to it.
• Define the enterprise technology standards and approved patterns for data platforms, model serving, retrieval, agent orchestration, integration, and observability — balancing best‑of‑breed flexibility against long‑term supportability.
• Maintain the architecture decision records (ADRs) and reference blueprints — predictive quality, energy and yield optimization, computer‑vision inspection, agentic workflow, digital twin — that Pods instantiate rather than reinvent.
• Lead build‑vs‑buy‑vs‑partner recommendations to the Steering Committee, including for the accelerated‑computing (GPU) stack, the data lakehouse, MLOps / LLMOps tooling, and Physical AI platforms.

Cross‑Pod Coordination & Delivery Governance

• Coordinate the portfolio of delivery Pods — managing cross‑Pod dependencies, shared‑component roadmaps, sequencing, and technical risk so initiatives compound rather than collide.
• Establish and run the intake‑to‑production pathway: architecture review, environment provisioning, promotion gates, and production‑readiness criteria that every use case passes through.
• Drive systematic reuse — harvest proven components (feature stores, connectors, prompt and agent frameworks, inspection models, twin services) into a shared catalog and require reuse before rebuild.
• Partner with the Responsible AI Lead to embed security, privacy, safety, and governance controls as non‑negotiable gates in the delivery pathway rather than after‑the‑fact reviews.

Platform, Data & MLOps / AgentOps Foundations

• Define the AI‑supporting data architecture — unified namespace, historian / PI, MES / ERP / quality integration, medallion and lakehouse layers, and data contracts — in concert with the data platform team.
• Set the MLOps, LLMOps, and AgentOps standards for the model and agent lifecycle: versioning, evaluation, monitoring, drift and cost management, and safe rollback.
• Establish the observability, FinOps, and performance‑engineering practices that keep AI — including on‑prem GPU workloads and agentic token economics — cost‑transparent and within budget.

OT / IT Convergence & Physical AI Integration

• Own the architecture for OT / IT convergence and edge‑to‑cloud data flow, ensuring plant‑floor systems (Level 1/2 automation, SCADA, PLCs, historians) integrate safely and deterministically with enterprise AI.
• Define reference architectures for Physical AI — industrial computer vision, robotics and autonomous material handling, sensor fusion, and real‑time digital twins — and the latency, safety, and reliability requirements they must meet.
• Ensure agentic workflows that touch operational or transactional systems act only through governed, auditable, permission‑scoped interfaces.
• Chair the enterprise AI Architecture Review Board; set the standards, review cadence, and exception process, and hold Pods accountable to them.
• Report architectural health, technical debt, reuse rates, and platform economics to the Steering Committee in terms leadership can act on.
• Mentor Pod architects and engineers, raising the enterprise's architectural maturity over time.

Required Qualifications & Experience

• 15+ years in enterprise / solution architecture, platform engineering, or industrial technology leadership, including significant time in manufacturing, heavy industry, or asset‑intensive operations.
• Demonstrated ownership of enterprise‑scale architectures spanning data platforms, ML / AI, and OT / IT integration — with production systems you can point to.

Deep, current command of the modern AI stack: data lakehouse, MLOps / LLMOps, RAG, agent‑orchestration frameworks, model serv

About the Office of AI

The Office of AI has been established to lead the enterprise-wide adoption of artificial intelligence, advanced analytics, industrial automation, and Physical AI across the company's steel, wire, and casting operations. The Office runs a "Core & Flex" operating model: a central Core team sets the shared platforms, standards, and guardrails, while domain‑aligned delivery Pods build and scale use cases close to the business. This role sits on the Core team and is a voting member of the AI Steering Committee — the cross‑functional body, including the business‑unit Presidents and enterprise leaders in Finance, HR, Operations, and Strategy, that sets AI priorities, approves investment, and governs risk. This is a hands‑on leadership role for a proven operator, not a purely advisory seat.

Role Mission

You own the enterprise AI reference architecture and the technical operating model that keep every AI, data, agentic, and Physical AI initiative coherent, reusable, secure, and scalable across all delivery Pods and all three business units. You are the single point of architectural accountability that prevents fragmentation, eliminates duplicated builds, and ensures that what one Pod proves becomes a shared capability the whole enterprise can reuse.

In the Core & Flex model, you are the guardian of the Core — the shared platform, standards, and guardrails — while enabling Pods to move fast within them. Success is measured not by how much technology is built, but by how much reliable value reaches production and how well it compounds over time.

Key Responsibilities

• Own and continuously evolve the enterprise target-state reference architecture for AI, data, and Physical AI spanning edge, plant, and cloud, and ensure every Pod solution maps to it.
• Define the enterprise technology standards and approved patterns for data platforms, model serving, retrieval, agent orchestration, integration, and observability — balancing best‑of‑breed flexibility against long‑term supportability.
• Maintain the architecture decision records (ADRs) and reference blueprints — predictive quality, energy and yield optimization, computer‑vision inspection, agentic workflow, digital twin — that Pods instantiate rather than reinvent.
• Lead build‑vs‑buy‑vs‑partner recommendations to the Steering Committee, including for the accelerated‑computing (GPU) stack, the data lakehouse, MLOps / LLMOps tooling, and Physical AI platforms.

Cross‑Pod Coordination & Delivery Governance

• Coordinate the portfolio of delivery Pods — managing cross‑Pod dependencies, shared‑component roadmaps, sequencing, and technical risk so initiatives compound rather than collide.
• Establish and run the intake‑to‑production pathway: architecture review, environment provisioning, promotion gates, and production‑readiness criteria that every use case passes through.
• Drive systematic reuse — harvest proven components (feature stores, connectors, prompt and agent frameworks, inspection models, twin services) into a shared catalog and require reuse before rebuild.
• Partner with the Responsible AI Lead to embed security, privacy, safety, and governance controls as non‑negotiable gates in the delivery pathway rather than after‑the‑fact reviews.

Platform, Data & MLOps / AgentOps Foundations

• Define the AI‑supporting data architecture — unified namespace, historian / PI, MES / ERP / quality integration, medallion and lakehouse layers, and data contracts — in concert with the data platform team.
• Set the MLOps, LLMOps, and AgentOps standards for the model and agent lifecycle: versioning, evaluation, monitoring, drift and cost management, and safe rollback.
• Establish the observability, FinOps, and performance‑engineering practices that keep AI — including on‑prem GPU workloads and agentic token economics — cost‑transparent and within budget.

OT / IT Convergence & Physical AI Integration

• Own the architecture for OT / IT convergence and edge‑to‑cloud data flow, ensuring plant‑floor systems (Level 1/2 automation, SCADA, PLCs, historians) integrate safely and deterministically with enterprise AI.
• Define reference architectures for Physical AI — industrial computer vision, robotics and autonomous material handling, sensor fusion, and real‑time digital twins — and the latency, safety, and reliability requirements they must meet.
• Ensure agentic workflows that touch operational or transactional systems act only through governed, auditable, permission‑scoped interfaces.
• Chair the enterprise AI Architecture Review Board; set the standards, review cadence, and exception process, and hold Pods accountable to them.
• Report architectural health, technical debt, reuse rates, and platform economics to the Steering Committee in terms leadership can act on.
• Mentor Pod architects and engineers, raising the enterprise's architectural maturity over time.

Required Qualifications & Experience

• 15+ years in enterprise / solution architecture, platform engineering, or industrial technology leadership, including significant time in manufacturing, heavy industry, or asset‑intensive operations.
• Demonstrated ownership of enterprise‑scale architectures spanning data platforms, ML / AI, and OT / IT integration — with production systems you can point to.
• Deep, current command of the modern AI stack: data lakehouse, MLOps / LLMOps, RAG, agent‑orchestration frameworks, model serving, and GPU / accelerated computing.
• Proven track record coordinating multiple concurrent delivery teams, managing dependencies, and enforcing standards without stifling delivery.
• Working, hands‑on understanding of manufacturing systems — MES, historians, SCADA / PLC, ISA‑95 Level 1–3 automation — and the realities of OT / IT convergence.
• Experience architecting at least one of: industrial computer vision, robotics / autonomous systems, or real‑time digital twins.

Preferred Qualifications

• Experience in steel, metals, foundry, or hybrid continuous / discrete manufacturing.
• Familiarity with unified namespace, MQTT / OPC-UA, and edge‑computing patterns.
• Track record standing up MLOps / AgentOps platforms and FinOps for AI and GPU workloads.
• Relevant certifications (e.g., TOGAF, cloud architecture, functional‑safety awareness) — welcome but secondary to demonstrated results.
• ing, and GPU / accelerated computing.
• Proven track record coordinating multiple concurrent delivery teams, managing dependencies, and enforcing standards without stifling delivery.
• Working, hands‑on understanding of manufacturing systems — MES, historians, SCADA / PLC, ISA‑95 Level 1–3 automation — and the realities of OT / IT convergence.
• Experience architecting at least one of: industrial computer vision, robotics / autonomous systems, or real‑time digital twins.

Preferred Qualifications

• Experience in steel, metals, foundry, or hybrid continuous / discrete manufacturing.
• Familiarity with unified namespace, MQTT / OPC-UA, and edge‑computing patterns.
• Track record standing up MLOps / AgentOps platforms and FinOps for AI and GPU workloads.
• Relevant certifications (e.g., TOGAF, cloud architecture, functional‑safety awareness) — welcome but secondary to demonstrated results.