Overview
At ST Engineering iDirect, the Senior Manager, AI & Enterprise Architecture is responsible for driving the technical realization of iDirect’s AI-powered Business Excellence transformation through a combination of hands-on execution and architectural leadership.
Reporting to the VP, Technology & Information Security, this role defines architectural direction while actively building and delivering AI-enabled solutions, integrations, and platform capabilities that reduce manual effort, improve decision-making, and accelerate time-to-value.
This is a player-coach role requiring both deep technical execution and the ability to set standards, guide architecture, and scale solutions across the enterprise.
Role Summary
This role owns both the design and delivery of AI-enabled solutions and enterprise architecture, ensuring business initiatives are translated into scalable, reusable, and measurable outcomes.
Key focus areas include:
Enterprise AI platform architecture and solution design
Hands-on development and deployment of AI/automation solutions
Business-driven solutioning tied to measurable outcomes
Platform standardization and reuse
Integration, data, and orchestration architecture
The role operates across architecture → build → scale, ensuring that what is designed is also successfully implemented and adopted.
Responsibilities
Key Responsibilities
Hands-On AI Solution Delivery (Core Expectation)
Design, build, and deploy AI-driven and automation solutions across business processes
Develop and implement use cases such as:
Bid management automation
Financial reporting optimization
Salesforce and customer workflow enhancements
Build solutions using:
AI platforms (Copilot, Dataiku, LLM-based services)
Workflow automation / orchestration tools
APIs and integration layers
Actively contribute to prototyping, development, and solution debugging
Drive rapid delivery of high-impact use cases (POC → production)
This role is expected to personally deliver and unblock critical solutions, not just direct teams
Enterprise Architecture & Solution Design
Define and evolve the enterprise AI platform architecture, including:
Unified data fabric
AI orchestration layer
API-first integration strategies
Translate business needs into end-to-end solution architectures and blueprints
Establish and enforce architecture principles, design patterns, and best practices
Ensure solutions are:
Scalable and reusable
Secure and compliant
Built on shared platforms (not siloed tools)
Business-Driven Solutioning
Partner with business stakeholders to translate prioritized initiatives into implementable solutions
Ensure all work ties directly to measurable KPIs:
Cycle time reduction
Cost savings
Productivity gains
CSAT / revenue impact
Provide technical leadership on high-priority Business Excellence initiatives
AI Platform Governance & Standardization
Establish and lead Architecture Review Board (ARB)
Enforce use of:
Approved platforms
Reusable components
Standardized integration patterns
Prevent tool proliferation and fragmented AI adoption
Drive consolidation into a centralized AI platform model
Data, Integration & Platform Engineering
Design and implement architecture across core enterprise systems:
Salesforce
SAP / Finance systems
Jira / Confluence
Build and guide:
API integrations
Event-driven workflows
Data pipelines and AI data layers
Enable cross-system intelligence and automation at scale
Delivery Acceleration & Enablement
Create reusable assets including:
Solution templates
Architecture patterns
AI and automation components
Standardize solution delivery to reduce:
Time-to-solution
Integration complexity
Act as a hands-on escalation point for complex technical challenges
Technology Governance & Quality
Ensure all solutions meet standards for:
Scalability
Security
Maintainability
Cost efficiency
Balance speed vs architectural integrity
Reduce long-term technical debt while enabling fast execution
Cross-Functional Leadership
Act as the technical bridge between business, ELT, and engineering teams
Influence prioritization using:
Value vs effort
Architectural tradeoffs
Drive alignment across Business Excellence, AI, DevOps, and IT functions
Ensure strong adoption and real business impact of delivered solutions
Qualifications
Qualifications
Experience
Bachelor’s degree in Computer Science, Engineering, or related field
8–12+ years in enterprise technology, solution architecture, or platform engineering
Proven experience building and delivering production-grade solutions (not just designing)
Experience leading architecture direction while remaining hands-on
Technical Expertise
Strong hands-on experience with:
AI/ML platforms (LLMs, copilots, AI services)
Automation frameworks and orchestration tools
API and integration development
Experience with:
Cloud platforms (Azure preferred)
Data pipelines and analytics platforms
Enterprise systems (Salesforce, ERP, etc.)
Understanding of:
Security, governance, and compliance in AI systems
DevOps / DevSecOps practices
Key Characteristics of Success
Player-coach mindset: leads by building, not just directing
Strong ability to move from idea → architecture → working solution → scale
Bias toward execution and measurable outcomes
Ability to enforce standards without slowing delivery
Strong systems thinking across process, data, and technology
Comfortable operating in ambiguity and driving clarity