DirectorTL, Embedded AI Architecture Lead

ROLE SUMMARY

As Director/TL Embedded AI Architecture Lead you will lead a highperforming team that designs and steers the solution architecture for internal AIpowered business products across ELT and ELT1 priorities. You will manage and grow a cohort of principallevel AI Solution Architects (ICs) and Technical Leads who mentor more junior AI Solution Designers/Engineers toward full AI Architects (system design software engineering AI engineering product mindset cloud engineering and data engineering).

Your mission is to deliver robust scalable composable AI solution architectures that combine stateoftheart AI/ML and AI Agents with modern software practices and enterpriseready integrations. You will ensure consistent reuse of SDKs modules and platform services drive design quality and evaluation rigor and create pathways that accelerate Solution Designers to become AI Solution Architects.

Why this Role Matters

This role is the connective tissue between innovation and repeatability for embedded AI delivery. You will turn promising PoCs and pilot patterns into reusable dependable designs that teams can implement at scale ensuring parity between SDKbased and servicebased consumption. You will tightly interface with the Platform team (for microservices and managed services) the Enterprise AI Capability Design Team (for modular AI capabilities and SDK ownership) the AI Architecture Operations Excellence (for process and metrics) and the AI Engineering department to create an endtoend architecture practice that is measurable governable and extensible across business domains.

Candidate Profile

You are a senior technical leader and people manager who blends architecture excellence with organizational enablement. Youre fluent across system design software engineering AI engineering and cloud architecture with a strong product mindset and the ability to translate complex stakeholder needsup to ELT and ELT1into clear scalable design choices. You mentor principal ICs while building a bench of Solution Designers on a structured growth path to Architect. You champion reusability evaluation rigor safety performance and cost efficiency and you thrive in matrixed environments where alignment and velocity go handinhand.

ROLE RESPONSIBILITIES

1) People Leadership & Capability Building

• Lead coach and performancemanage principallevel AI Solution Architects as highimpact ICs.
• Manage a Technical Lead who mentors Solution Designers/Engineers; ensure clear apprenticeship and rotation pathways toward full AI Architect readiness.
• Define competency models growth plans and objective skill assessments (in partnership with Operations Excellence).
• Foster a culture of technical excellence documentation quality and knowledge sharing via ceremonies reviews and exemplars.

2) Architecture Ownership & Delivery Excellence

• Own the embedded AI solution architecture across prioritized ELT/ELT1 use cases; ensure designs are modular decoupled composable and futureproof.
• Define and steward internal solution patterns reference architectures and templates for common AI scenarios (e.g. RAG/document intelligence agentic workflows predictive modeling LLMdriven interfaces streaming inference).
• Ensure parity and reusability across SDKs modules and platform microservices; maximize leverage of enterprise platform capabilities and guard against bespoke oneoffs.
• Partner with the Platform team on service boundaries SLOs observability security controls and cost/performance design tradeoffs.

3) Evaluation Safety & Governance

• Own the solution evaluation methodology (metrics harnesses and guardrails) covering quality safety bias robustness observability and reproducibility.
• Drive architecture reviews and participate in governance boards; ensure alignment with enterprise architecture principles without duplicating platform ownership.
• Embed security compliance privacy and data protection by design; ensure rigorous auditability and release documentation in line with documentation standards.

4) CrossTeam Collaboration & Reuse

• Collaborate with Applied AI Engineering / AI Innovation to convert PoC learning into hardened reusable solution patterns and exemplars.
• Partnerwith the Enterprise AI Capability Design Team to influence the capability roadmap (SDKs/modules) ensuring designs are extensible and serviceready.
• Contribute to and consume the catalogue of reusable SDKs modules and platform offerings; identify net-new capability gaps and propose solutions.

5) Operations Process & Metrics

• Work with Operations Excellence to continuously refine the AI Architecture Design process; uphold ceremonies artefacts and quality bars.
• Define and track team KPIs (e.g. reuse rates cycle time to architecture approval timetofirstvalue cost/performance targets documentation completeness evaluation coverage).
• Maintain operational capture (decisions patterns ADRs exemplars) and ensure process changes are evaluated and approved by department leads.
• Champion enablement content (guides blueprints code samples) that accelerates internal teams and raises the quality and consistency of delivery.

QUALIFICATIONS

BASIC QUALIFICATIONS

• 10 years in applied AI/ML and/or largescale software/system architecture with substantial experience leading solution architecture in production or nearproduction settings.
• Proven record leading principallevel ICs and mixedseniority teams (Technical Leads Solution Designers/Engineers) in a matrixed enterprise.
• Deep understanding of the endtoend AI solution lifecycle (data modeling evaluation deployment monitoring and continuous improvement) including LLMcentric and traditional ML workloads.
• Demonstrated ability to convert PoCs into repeatable patterns balancing speed safety cost and performance.
• Experience operating within enterprise architecture governance security and compliance frameworks.
• Exceptional communication skills; comfortable engaging ELT/ELT1 stakeholders driving alignment and narrating value to the enterprises mission.

PREFERRED QUALIFICATIONS

• Experience in largescale regulated industries (e.g. healthcare pharma finance) and with GxPlike quality expectations.
• Handson familiarity with GenAI/LLM ecosystems (prompt engineering RAG retrieval strategies agents tooluse
• function calling) vector databases and evaluation frameworks.
• Strong background in modern software architecture: cloudnative microservices event-driven streaming APIs SDK design CI/CD observability security controls cost optimization and performance engineering.
• Experience defining internal technical standards reference architectures and architecture documentation practices at scale.
• Exposure to MLOps/LangOps toolchains policy/runtime guardrails monitoring for AIspecific failure modes and data governance patterns.

Work Location Assignment:Hybrid