Director/TL, Embedded AI Architecture Lead

ROLE SUMMARY

As Director/TL, Embedded AI Architecture Lead, you will lead a high‑performing team that designs and steers the solution architecture for internal AI‑powered business products across ELT and ELT‑1 priorities. You will manage and grow a cohort of principal‑level 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 state‑of‑the‑art AI/ML and AI Agents with modern software practices and enterprise‑ready 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 SDK‑based and service‑based 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 end‑to‑end 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. You’re fluent across system design, software engineering, AI engineering, and cloud architecture, with a strong product mindset and the ability to translate complex stakeholder needs—up to ELT and ELT‑1—into 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 hand‑in‑hand.

ROLE RESPONSIBILITIES

1) People Leadership & Capability Building

• Lead, coach, and performance‑manage principal‑level AI Solution Architects as high‑impact 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/ELT‑1 use cases; ensure designs are modular, decoupled, composable, and future‑proof.
• Define and steward internal solution patterns, reference architectures, and templates for common AI scenarios (e.g., RAG/document intelligence, agentic workflows, predictive modeling, LLM‑driven interfaces, streaming inference).
• Ensure parity and reusability across SDKs, modules, and platform microservices; maximize leverage of enterprise platform capabilities and guard against bespoke one‑offs.
• Partner with the Platform team on service boundaries, SLOs, observability, security controls, and cost/performance design trade‑offs.

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) Cross‑Team 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 service‑ready.
• 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, time‑to‑first‑value, 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 large‑scale software/system architecture, with substantial experience leading solution architecture in production or near‑production settings.
• Proven record leading principal‑level ICs and mixed‑seniority teams (Technical Leads, Solution Designers/Engineers) in a matrixed enterprise.
• Deep understanding of the end‑to‑end AI solution lifecycle (data, modeling, evaluation, deployment, monitoring, and continuous improvement), including LLM‑centric 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/ELT‑1 stakeholders, driving alignment, and narrating value to the enterprise’s mission.

PREFERRED QUALIFICATIONS

• Experience in large‑scale, regulated industries (e.g., healthcare, pharma, finance) and with GxP‑like quality expectations.
• Hands‑on familiarity with GenAI/LLM ecosystems (prompt engineering, RAG, retrieval strategies, agents, tool‑use,
• function calling), vector databases, and evaluation frameworks.
• Strong background in modern software architecture: cloud‑native 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 AI‑specific failure modes, and data governance patterns.

 
Work Location Assignment: Hybrid

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