Artificial Intelligence Architect

AI Solution Architect &

Delivery Manager

Function: Enterprise Technology / AI & Digital Transformation

Role Level: Senior Individual Contributor / Solution & Delivery Lead

Experience: 8-10 years overall technology experience, with strong AI solutioning and delivery experience

Primary Focus: AI Solution Architecture | Business Solutioning | Project Delivery | GenAI & Agentic AI | Azure AI | Copilot | AI

Governance

Role Overview

We are seeking an experienced AI Solution Architect & Delivery Manager who can own AI & Digitization initiatives end-to-end,

from understanding the business problem and solutioning through development, implementation, production rollout and

continuous improvement.

This is not a pure architecture or advisory role. The individual will work directly with business stakeholders to understand

requirements, assess the applicability of AI, translate business needs into practical solution designs, and work closely with

internal developers, engineering teams and external partners to ensure successful delivery.

The role will also be responsible for day-to-day execution of the AI project portfolio, including project planning, milestone

tracking, dependency management, risk and issue resolution, stakeholder updates, UAT, production readiness and closure.

Given the rapidly evolving AI landscape, the individual is expected to continuously evaluate developments across Generative

AI, Agentic AI, Microsoft Copilot, Azure AI, LLMs, orchestration frameworks and emerging AI technologies, and identify

opportunities to improve existing solutions or adopt more effective technology approaches.

The role will operate at the intersection of Business, Architecture and Delivery, providing a single point of accountability for

translating AI ideas into scalable, secure and business-ready solutions.

Key Responsibilities

Business Engagement & Requirement Solutioning

• Partner directly with business leaders, process owners and functional teams to understand business problems, pain

points and desired outcomes.

• Translate business requirements into clear AI use cases, functional requirements and technical solution approaches.

• Challenge requirements where appropriate and determine whether AI is the right solution or whether automation,

workflow, analytics or conventional application development would be more suitable.

• Conduct discovery workshops and define scope, expected outcomes, dependencies, business value and success

criteria.

• Translate complex AI concepts into simple business language so business stakeholders can make informed

decisions.

• Develop prototypes or coordinate POCs to validate feasibility before committing to full-scale development.

End-to-End AI Delivery Management

• Take end-to-end ownership of assigned AI initiatives from requirement through production rollout.

• Develop and maintain project plans covering scope, timelines, milestones, dependencies and resources.

• Run day-to-day project execution with developers, business analysts, testers, infrastructure teams, InfoSec and

external vendors.

• Track progress across multiple concurrent AI projects and proactively identify slippages, blockers and dependencies.

• Coordinate development activities and ensure that technical teams clearly understand requirements and acceptance

criteria.

• Conduct regular delivery reviews and drive closure of actions.

• Coordinate POCs, development, testing, UAT, production readiness and deployment.

• Ensure business stakeholders remain engaged through demonstrations, feedback cycles and acceptance testing.

• Manage defects, change requests and scope changes through to closure.

• Provide concise RAG/status reporting, highlighting progress, risks, dependencies and decisions required.

• Take accountability for delivery outcomes rather than limiting responsibility to architectural recommendations.

Development Team & Vendor Coordination

• Work closely with AI engineers, Python developers, application developers and platform teams to convert solution

designs into working products.

• Break business requirements into implementable technical work packages.

• Provide architecture and technical guidance during development without becoming only a review layer.

• Review development outputs, prototypes and technical decisions at appropriate checkpoints.

• Coordinate work delivered through external vendors and OEM partners.

• Challenge vendor estimates, proposed architecture and technology choices where required.

• Ensure vendor-delivered solutions meet agreed requirements, enterprise standards and production-readiness criteria.

Continuous AI Technology Evolution

• Continuously monitor developments across Generative AI, Agentic AI, Copilot, LLMs, orchestration frameworks, model

hosting and emerging AI platforms.

• Assess whether new AI capabilities can improve existing solutions in terms of accuracy, user experience,

performance, scalability or cost.

• Periodically review deployed AI solutions and recommend technology or architecture upgrades.

• Evaluate new features from strategic technology partners and determine their applicability to business use cases.

• Run controlled experiments and POCs for emerging technologies before enterprise adoption.

• Prevent solutions from becoming locked into obsolete AI architecture or models.

• Maintain reusable patterns and reference architectures as the technology landscape evolves.

AI Governance & Production Readiness

• Ensure solutions follow the firm's applicable Architecture Review Process (ARP) and AI governance requirements.

• Build security, privacy, data protection, access management, monitoring and human oversight considerations into

solution design.

• Support the firm's ISO/IEC 42001 AI management requirements.

• Ensure appropriate Dev/UAT/Production separation and controlled deployment.

• Maintain architecture decisions and technical documentation needed for governance and audit.

• Balance governance requirements with speed of delivery and practical business outcomes.

• Promote reuse of existing AI capabilities and prevent unnecessary duplication.

Required Experience & Capabilities

• Operational considerations earlier in the lifecycle to reduce rework and improve delivery predictability.

• Ability to manage developers and delivery partners and drive execution without requiring a separate project manager.

• Strong understanding of APIs, Python-based AI applications, microservices and modern integration architecture.

• Experience managing POCs, UAT, production rollout and post-production enhancements.

• Strong written and verbal communication skills with the ability to explain complex technical choices in business

language.