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 co‑drives high‑bar 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.

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 well‑engineered SDKs/modules, you multiply delivery velocity across business products while raising quality and safety. Clear SDK↔Service parity, rigorous testing and evaluation, and developer‑first DX reduce bespoke one‑offs, 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 hands‑on 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 cross‑functional, matrixed environments where platform alignment and reuse are paramount.

ROLE RESPONSIBILITIES

Main responsibilities, but are not limited to:

• 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 etc
• Drive API and SDK architecture: consistent patterns, semantic versioning, backward compatibility policies, error models, dependency rules, and language support strategy.
• Ensure SDK↔Service parity: author service interface contracts and service readiness specs (SLOs, scalability envelopes, observability and security controls) for Platform implementation.
• Make rigorous testing non‑negotiable: unit, integration, contract, fuzz/adversarial, performance/regression, and security tests wired into CI/CD.
• Embed AI‑specific evaluation: datasets, metrics, harnesses, red‑teaming 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.
• 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., SLSA‑like controls).
• Partner with Embedded AI Architecture to translate solution patterns into reusable capabilities; close gaps discovered in product engagements.
• Lead, coach, and performance manage Software Engineers and DevOps Engineers; mentor the AI Architect (IC) as technical bar‑raiser.

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 cross‑language delivery.
• Proven track record instituting rigorous testing and evaluation, including performance engineering and security controls for AI‑enabled 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

• Hands‑on familiarity with GenAI/LLM ecosystems (prompt tooling, RAG strategies, vector stores, agent frameworks, evaluation tooling) and traditional ML integration patterns.
• Background in cloud‑native architecture: microservices, eventing/streaming, APIs, containers/orchestration, and cost/performance optimization.
• Experience in regulated or high‑assurance environments (healthcare, pharma, finance) and audit‑ready 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 open‑source‑style contribution workflows internally.

Work Location Assignment: Hybrid