AI Architect

Company Description

IFS is a billion-dollar revenue company with 7000+ employees on all continents. Our leading AI technology is the backbone of our award-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™. Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting-edge.

At IFS, we’re flexible, we’re innovative, and we’re focused not only on how we can engage with our customers but on how we can make a real change and have a worldwide impact. We help solve some of society’s greatest challenges, fostering a better future through our agility, collaboration, and trust.

We celebrate diversity and understand our responsibility to reflect the diverse world we work in. We are committed to promoting an inclusive workforce that fully represents the many different cultures, backgrounds, and viewpoints of our customers, our partners, and our communities. As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view.

By joining our team, you will have the opportunity to be part of a global, diverse environment; you will be joining a winning team with a commitment to sustainability; and a company where we get things done so that you can make a positive impact on the world.

We’re looking for innovative and original thinkers to work in an environment where you can #MakeYourMoment so that we can help others make theirs. With the power of our AI-driven solutions, we empower our team to change the status quo and make a real difference.

If you want to change the status quo, we’ll help you make your moment. Join Team Purple. Join IFS.

Job Description

Position Summary

The AI Architect is responsible for maintaining architectural coherence across GCS AI and Frictionless Time to Value (FTTV) initiatives. The role works at the front end of the process, participating in use-case and initiative shaping, backlog review, and initial solution definition before significant engineering effort is committed.

The architect reviews proposed AI applications and FTTV solutions to determine whether they align with GCS strategy, fit the existing application, process, and tooling landscape, and make appropriate use of existing knowledge, integrations, services, and reusable components. Working closely with AI Engineering and FTTV stakeholders, the architect helps define standards, reusable patterns, solution boundaries, and cross-initiative connection points.

This is a Senior Lead-level individual contributor role. It does not manage an engineering squad or replace AI Engineering accountability for detailed design, implementation, code quality, production readiness, or delivery assurance. The architect is not expected to be the sole expert in every technical domain, but is responsible for ensuring that the appropriate specialists review the solution.

Key Responsibilities

GCS AI and FTTV Solution Shaping

• Participate in the review and maintenance of the complete GCS AI backlog and the shaping of FTTV initiatives, with particular focus on work entering solution definition.

• Assess whether proposed applications and FTTV solutions support GCS strategy, process direction, and the intended tooling landscape.

• Identify duplicate, overlapping, unnecessarily isolated, or technically unclear proposals across the GCS AI backlog and FTTV initiatives before significant engineering effort is committed.

• Work with business stakeholders and AI Engineering to refine use cases, clarify solution boundaries, and identify dependencies, integration points, and ownership.

• Provide architecture input into GCS AI backlog and FTTV initiative decisions without assuming ownership of business prioritization.

Reuse, Integration, and Knowledge

• Identify where GCS AI applications and FTTV solutions should reuse existing services, integrations, knowledge assets, evaluation approaches, or common components.

• Work with AI Engineering to determine when a capability should be shared and when an application-specific implementation is more maintainable.

• Identify required connection points between solutions and ensure their purpose, ownership, and expected behavior are understood before implementation.

• Review how structured knowledge is created, organized, accessed, and reused across AI applications and related FTTV solutions.

• Promote consistent approaches to knowledge ownership, source traceability, permissions, versioning, update responsibility, and retirement.

• Challenge solution-specific knowledge stores when an appropriate governed source exists or is planned, while avoiding reuse that creates hidden coupling or unclear ownership.

Architecture Standards and Exceptions

• Work collaboratively with AI Engineering leads and developers to define practical architecture standards, reference patterns, and reusable approaches.

• Distinguish between mandatory constraints, preferred patterns, implementation guidance, and examples.

• Review how agreed standards are applied during solution shaping and propose changes when standards are unclear, outdated, or produce unintended results.

• Identify and document justified exceptions, including their scope, consequences, dependencies, and support implications.

• Present proposed exceptions for approval to Customer AI Experience leadership.

• Treat recurring exceptions as evidence that a standard may need to change rather than repeatedly bypassing it.

Specialist Review and Technical Alignment

• Determine which specialist perspectives are required for each proposed solution and involve them early enough to affect the design.

• Ensure appropriate review of material decisions involving security, privacy, responsible AI, enterprise architecture, data, integrations, infrastructure, business processes, and product platforms.

• Bring conflicting recommendations to the appropriate decision-makers with the options and trade-offs clearly stated.

• Confirm that specialist recommendations are reflected in the proposed architecture or are addressed through a documented and approved exception.

• Avoid making decisions on behalf of specialist functions where authority or expertise sits elsewhere.

Collaboration with AI Engineering and FTTV Stakeholders

• Act as an architectural counterpart to AI Engineering leads and FTTV stakeholders during initiative shaping and initial solution definition.

• Provide architectural direction without assuming responsibility for squad management or engineering delivery.

• Work directly with developers when technical detail is required to evaluate a design, dependency, reuse opportunity, or proposed standard.

• Use engineering and stakeholder feedback to test whether proposed standards and reusable approaches are practical.

• Support consistency across initiatives and engineering squads without introducing an additional approval layer for routine implementation decisions.

Technical Validation and Decision Traceability

• Remain technically current enough to challenge assumptions and evaluate proposed designs credibly.

• Prototype selected concepts, inspect implementations, or develop reference examples when needed to validate an architecture decision.

• Use technical validation selectively; the role is not intended to provide routine development capacity.

• Maintain a current view of the GCS AI and FTTV solution landscape, including major boundaries, shared capabilities, dependencies, integrations, and knowledge relationships.

• Document material architecture decisions, rationale, ownership, and approved exceptions at a level proportionate to the decision.

Strategic Alignment with Processes and Tooling

• Assess whether proposed AI applications and FTTV solutions support the intended direction of GCS business processes and operational tooling.

• Work with process, tooling, and functional owners to understand planned changes that may affect solution design.

• Identify proposals that automate temporary workarounds, reproduce process defects, or create competing sources of process logic.

• Make unresolved process or tooling dependencies visible before engineering commits to an approach.

• Support a coherent GCS AI and FTTV solution portfolio that can evolve as GCS processes and tooling change without requiring each application or initiative to be independently redesigned.

Qualifications

Required Experience

• Significant experience in software, solution, application, integration, data, or AI architecture across multiple applications or engineering teams.

• Experience shaping technical solutions before implementation and assessing application boundaries, dependencies, integration points, and reuse opportunities.

• Practical experience with AI-enabled applications, including large language models, retrieval-based solutions, agentic systems, and structured and unstructured knowledge.

• Strong understanding of modern software architecture, APIs, distributed systems, cloud services, identity, security, and integration approaches.

• Experience defining architecture standards and reusable patterns collaboratively with engineers.

• Ability to identify duplication, hidden coupling, weak ownership models, and design choices that create long-term maintenance problems.

• Ability to evaluate and explain technical trade-offs to engineering, business, and leadership audiences.

• Ability to work through influence without relying on line authority and to distinguish portfolio-level concerns from engineering implementation decisions.

• Sufficient hands-on technical capability to prototype concepts, inspect implementations, and validate architectural assumptions when necessary.

• Sound judgment about when to decide, when to involve a specialist, and when to escalate an unresolved trade-off.

Preferred Experience

• Experience designing or reviewing enterprise AI applications in production.

• Experience with retrieval-augmented generation, agent architectures, shared knowledge services, evaluation frameworks, and AI observability.

• Experience with Azure AI services, Azure OpenAI, Azure AI Foundry, or comparable enterprise AI platforms.

• Experience with API-first, event-driven, microservice, and cloud-native solution patterns.

• Experience defining reusable components or platform capabilities across multiple teams.

• Experience with enterprise business applications, ERP, customer delivery, or service-delivery processes.

• Familiarity with GCS, FTTV, delivery tooling, and AI adoption priorities.

• Experience operating at Senior Lead, Principal, or equivalent individual-contributor level.

Additional Information

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.