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