DirectorTL, AI Capability Architecture Lead

ROLE SUMMARY

As Director/TL AI Capability Architecture Lead you will lead the Enterprise AI Capability Design Team accountable for designing building and evolving modular reusable AI capabilities (SDKs/modules) that can be embedded into application backends or consumed as platform services with feature parity. You will manage a team of Software Engineers and DevOps Engineers and you will include/principally mentor an AI Architect (IC) who codrives highbar technical design. Your scope spans capability roadmap stewardship API/SDK design and versioning quality engineering and evaluation observability-by-design and secure-by-default patterns that the Platform team can extend into microservices and managed services.

You will ensure these capabilities are extensible composable and performant and that they integrate cleanly with enterprise controls and telemetry. You will partner closely with the Platform team (for service implementation) the Embedded AI Architecture Team (for solution patterns and field feedback) AI Engineering (for model/agentic expertise and rotation) and AI Architecture Operations (for process metrics and documentation standards).

Why this Role Matters

This is the productization engine for reusable AI. By turning proven patterns (e.g. RAG document intelligence agentic workflows evaluation harnesses prompt tooling safety filters) into wellengineered SDKs/modules you multiply delivery velocity across business products while raising quality and safety. Clear SDKService parity rigorous testing and evaluation and developerfirst DX reduce bespoke oneoffs lower total cost of ownership and make AI features operable at enterprise scale. You create the building blocks that embedded architecture and product teams can adopt with confidence.

Candidate Profile

You are a handson architectural leader who blends deep software engineering with AI capability design. You are fluent in API/SDK design packaging/versioning performance engineering and security-by-design and you know how to translate applied AI methods into composable testable observable modules. You are passionate about developer experience (DX) you maintain high code and documentation standards and you thrive in crossfunctional matrixed environments where platform alignment and reuse are paramount.

ROLE RESPONSIBILITIES

1) Capability Ownership & Technical Direction

• Help define and own the capability portfolio and roadmap (e.g. RAG components document intelligence evaluation toolkits prompt and safety utilities agent tooling vector/connectors feature stores model/endpoint clients).

• Drive API and SDK architecture: consistent patterns semantic versioning backward compatibility policies error models dependency rules and language support strategy.

• Ensure SDKService parity: author service interface contracts and service readiness specs (SLOs scalability envelopes observability and security controls) for Platform implementation.

• Establish reference implementations and exemplars that demonstrate correct integration performance envelopes and guardrail usage.

2) Engineering Quality Testing & Evaluation

• Make rigorous testing nonnegotiable: unit integration contract fuzz/adversarial performance/regression and security tests wired into CI/CD.

• Embed AIspecific evaluation: datasets metrics harnesses redteaming hooks and reproducibility standards (seeds data/version pins).

• Define quality gates for release: evaluation coverage thresholds latency/throughput budgets cost envelopes security/privacy checks and documentation completeness.

• Partner with AI Engineering to validate model and agentlevel behaviors and to capture failure modes and mitigations.

3) Observability Security & Compliance by Design

• Bake in telemetry and diagnostics: structured logs traces metrics and debug hooks that flow to enterprise observability stacks; define SLIs/SLOs and error budgets.

• Lead threat modeling and security controls (authn/z secrets key management data minimization PII handling abuse safeguards rate limiting).

• Own supply chain hygiene: SBOMs artifact signing dependency policies license scanning and vulnerability management; align to internal standards (e.g. SLSAlike controls).

• Ensure documentation and evidence support auditability (compliance privacy and regulated-context expectations).

4) Developer Experience (DX) & Enablement

• Set the DX bar: quickstarts sandbox projects code samples 10minute Hello World and taskfocused guides.

• Maintain API docs and design narratives with Technical Writers (via Operations): diagrams ADRs changelogs and deprecation notices.

• Run capability showcases and office hours; capture field feedback and close the loop into the roadmap.

5) CrossTeam Collaboration & Reuse

• Partner with Embedded AI Architecture to translate solution patterns into reusable capabilities; close gaps discovered in product engagements.

• Partner with Applied AI Engineering department for the development of PI of the SDKs.

• Codesign service boundaries and readiness with the Platform team; ensure consistent operability security and cost/performance characteristics.

• Coordinate with Operations Excellence on playbook alignment artefacts and KPI instrumentation; publish regular capability health reports.

• Support rotation programs with AI Engineers/Software Engineers to broaden expertise and pipeline future capability owners.

6) People Leadership & Org Health

• Lead coach and performance manage Software Engineers and DevOps Engineers; mentor the AI Architect (IC) as technical barraiser.

• Define competency models and growth paths (architecture performance reliability security evaluation) with Operations Excellence.

• Foster a culture of clean design measurable quality and documentation excellence; celebrate reuse and contributions across teams.

7) Tooling CI/CD & Release Management

• Own the build and release system for SDKs/modules: pipelines artifact repositories package registries versioning and release trains.

• Automate quality gates and evidence capture in CI/CD (evaluation reports perf baselines SBOMs test coverage).

• Define deprecation and migration policies; provide safe upgrade paths and tooling (linters codemods compatibility shims).

BASIC QUALIFICATIONS

• 10 years in software architecture/engineering with significant experience building SDKs/libraries/platform modules and partnering with platform/service teams.

• Demonstrated ownership of API/SDK design semantic versioning compatibility strategies packaging and crosslanguage delivery.

• Proven track record instituting rigorous testing and evaluation including performance engineering and security controls for AIenabled components.

• Experience designing for observability (SLIs/SLOs tracing/metrics/logging) and establishing release quality gates in CI/CD.

• Exceptional communication skills; able to align architects platform engineers and product stakeholders around capability roadmaps and standards.

PREFERRED QUALIFICATIONS

• Handson familiarity with GenAI/LLM ecosystems (prompt tooling RAG strategies vector stores agent frameworks evaluation tooling) and traditional ML integration patterns.

• Background in cloudnative architecture: microservices eventing/streaming APIs containers/orchestration and cost/performance optimization.

• Experience in regulated or highassurance environments (healthcare pharma finance) and auditready documentation/change control.

• Exposure to MLOps/LangOps stacks policy/guardrail integration and data governance patterns relevant to AI capabilities.

• Demonstrated leadership building developer experience programs (docs samples trainings) and running opensourcestyle contribution workflows internally.

Work Location Assignment:Hybrid