Job Description

Our Journey So Far
We started as an Engineering, Procurement, and Construction (EPC) company driven by one mission to deliver projects that unite technical excellence, safety, and sustainability through digital intelligence. Over time, we’ve evolved from a traditional contractor into a data-driven innovation hub, connecting design, construction, and operations with cutting-edge technology. Today, our teams manage complex industrial and infrastructure projects. By embedding AI-powered analytics, digital twins, and intelligent contract systems into every phase of the EPC lifecycle, we’re building a smarter, more connected, and more transparent future for engineering.

Why Join Us
We’re entering a new era of engineering where data, AI, and collaboration redefine how projects are designed, managed, and delivered. By combining engineering expertise with digital innovation, we’re creating the next generation of EPC systems from AI-assisted design validation and smart contract analysis to real-time digital twins and project intelligence dashboards. Joining us means helping shape the digital backbone of modern infrastructure, where your ideas directly influence decisions from boardrooms to construction sites, driving safer, smarter, and more sustainable outcomes.

Role Mission
The Systems & Methods Architect is responsible for designing and governing the enterprise-wide workflow architecture across all organizational functions.
This role defines how work flows across the company—ensuring:

• Structural consistency
• Strategic alignment
• Risk-based controls
• AI-enabled but human-accountable decision logic
• Data governance and auditability
• Developer-ready implementation clarity

This role owns the architecture and governance of enterprise workflows; implementation execution is performed in collaboration with digital and development teams.

This is not a documentation role.
This is an enterprise control-system architecture role.

Scope of Responsibility
The objective is to build a unified, interoperable, and scalable enterprise process architecture that replaces fragmented procedures with an integrated, risk-based, and strategically governed operating model.
The Architect ensures that workflows across all functions operate as a coherent system—aligned to enterprise strategy, governance principles, digital transformation objectives, and responsible AI integration.

Core Responsibilities

1. Enterprise Process Architecture

• Develop and maintain enterprise process maps (L0–L3) with defined ownership and interfaces.
• Standardize workflow design patterns and lifecycle structures (request → review → approval → execution → evidence → closure).
• Establish naming conventions, state definitions, transition rules, and reusable templates.
• Ensure structural consistency and interoperability across all departments.
• Clarify the role of each system in end-to-end workflows and define responsibility boundaries between the process, the system, and the organizational unit (RACI-by-design at workflow touchpoints).
• Maintain a workflow interface registry capturing where processes cross systems and teams, including accountable owners, required inputs/outputs, and evidence artifacts.

 
2. Governance & Decision Architecture

• Embed decision rights, authority matrices, and approval logic into workflows.
• Define when separation-of-duties (Maker/Checker) controls are required.
• Establish structured exception governance and escalation models.
• Ensure workflows produce verifiable and auditable evidence.
• Define measurable KPIs and SLA/OLA logic aligned with governance objectives.
• Design a change-impact governance framework to evaluate process changes → system impact and system changes → operational impact (risk, continuity, compliance).
• Define risk-tiered change paths and governance guardrails (streamlined low-risk changes; explicit controls and evidence for high-risk changes).
• Define end-to-end event logging and decision trace requirements to make workflows measurable, auditable, and analyzable.
• Use workflow execution data (event logs) to identify bottlenecks, rework loops, and compliance gaps, and translate findings into structural improvements.

 
3. Risk-Based Process Design

• Embed risk identification and control mechanisms directly into workflow architecture.
• Design preventive, detective, and corrective controls.
• Define escalation thresholds and residual risk handling.
• Align process architecture with enterprise resilience and infrastructure optimization objectives.
• Ensure workflows remain robust under stress, failure, and exception scenarios.

 
4. AI-Enabled Workflow Governance

• Identify appropriate AI integration points (e.g., classification, extraction, summarization, triage, risk flagging, decision support).
• Define AI-human interaction models with clear human-in-the-loop accountability.
• Specify input/output logic, confidence thresholds, override authority, and logging requirements.
• Ensure AI-assisted decisions remain explainable, traceable, and auditable.
• Define AI failure handling and fallback mechanisms.
• Maintain an AI use-case/model inventory (owner, purpose, data inputs/outputs, risk class, required controls, and approval status).
• Define evaluation and acceptance criteria for AI-assisted steps (quality targets, error tolerance, escalation rules, and human override conditions).
• Define monitoring signals and incident response for AI behaviors in production (drift, failure patterns, rollback and postmortem requirements).

5. Data Governance Integration

• Define data ownership within workflows.
• Establish access control logic (RBAC principles).
• Ensure audit trails for all state transitions and decision events.
• Design data lifecycle controls (creation, storage, usage, archival).
• Align workflows with data classification, security, and compliance principles.
• Design and standardize integration points between systems at workflow intersections to prevent duplication, rework, and information discontinuity.
• Define the logical structure of data exchange and synchronization rules between systems throughout the workflow (source-of-truth, reconciliation, conflict resolution).
• Introduce data contracts for critical workflow entities and events (schema, semantics, constraints, quality rules, versioning, and backward compatibility expectations).
• Define data quality requirements and stewardship expectations for key workflow data (completeness, accuracy, timeliness), including metadata and lineage needs where required for auditability.

 
6. Developer-Ready Requirements Engineering

• Translate enterprise workflows into clear, structured, and testable implementation specifications suitable for digital systems.
• Collaborate closely with development teams to ensure feasibility, consistency, and alignment between process intent and system design.
• Support solution validation, user acceptance readiness, and implementation quality assurance.
• Create a shared language and operating model between business and IT for workflow design, change decisions, and prioritization of system enhancements (process intent → requirements → acceptance tests).
• Define control test cases (including negative and exception scenarios) to validate evidence outputs, audit logs, segregation-of-duties, and gating logic.

 
7. Standards & Best Practice Alignment
Demonstrated practical familiarity with applicable standards and governance frameworks, including:

• ISO 9001 (process-based management, document control, CAPA)
• ISO 31000 (risk management integration)
• ISO 21502 (project governance alignment)
• ISO/IEC 27001 (information security and audit controls)
• ISO/IEC 20000-1 / ITIL concepts (incident/problem/change logic)
• Awareness of AI governance frameworks (e.g., ISO/IEC 42001, NIST AI RMF principles)
• Awareness of enterprise IT governance concepts (e.g., decision rights, monitoring/assurance models such as COBIT principles).
• Certification is not mandatory; applied knowledge and implementation experience are required.

 
Key Deliverables

Enterprise process maps (L0–L3) with defined ownership, interfaces, and reusable lifecycle patterns.
Workflow control-pattern library (Maker/Checker, escalation, evidence, exception handling, segregation-of-duties patterns).
Workflow interface registry and integration blueprint across systems (touchpoints, responsibilities, inputs/outputs, evidence).
Change-impact assessment framework (process <-> system) with risk-tiered change paths and governance guardrails.
Data exchange and synchronization rules, including data contracts for critical workflow entities and events.
AI governance pack: AI use-case/model inventory, evaluation/acceptance criteria, monitoring signals, and incident response workflow.
KPI model and bottleneck analysis approach based on workflow event logs (cycle time, rework, control effectiveness).
 
Measures of Success

Priority workflows standardized with defined owners, interfaces, controls, and evidence outputs.
Reduced cycle time and rework due to clarified responsibilities, standardized integrations, and improved data consistency.
Improved audit readiness: evidence completeness, traceability of decisions, and fewer control gaps.
Lower change failure rate through risk-tiered change governance and impact assessment.
AI-assisted steps operate within defined thresholds with monitoring, escalation, and documented incident resolution.
 
Required Background

5+ years in enterprise process architecture, systems & methods, digital transformation, or workflow governance roles.
Experience designing workflows across multiple departments and functions.
Proven ability to align operational processes with strategic governance objectives.
Experience collaborating effectively with software development teams.