Senior Manager, AI & Enterprise Architecture

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

Originally posted on Himalayas