The Director, AI Solutions Architect is a senior technical leader responsible for translating enterprise AI strategy into scalable, secure, and production‑ready solutions. Reporting to the Senior Director, Software Engineering, this role serves as the connective tissue between strategy and execution—owning solution architecture, technical standards, and delivery excellence for AI‑enabled products across the organization.
This leader works side by side with Product, Design, Engineering, Security, and Platform teams to deliver AI‑driven solutions that delight customers and accelerate time to value—while balancing feasibility, scalability, cost, and compliance. The Director sets architectural direction, coaches teams, and remains hands‑on where it matters most, ensuring the organization applies AI responsibly and effectively to real business problems.
You will guide engineering teams on when and how to apply AI capabilities—copilots, agents, and vendor integrations—while enforcing architectural guardrails and elevating engineering maturity. You translate vision into architecture, patterns, and working software that deliver measurable outcomes. You are equally comfortable influencing executives and diving into code with teams to unblock delivery.
Key Responsibilities
- Own end‑to‑end solution architecture for AI and AI‑enabled products (discovery → design → deployment), ensuring security, reliability, cost efficiency, and maintainability across cloud and on‑prem environments.
- Serve as the architecture authority for GenAI and applied AI solutions, approving designs and ensuring alignment with enterprise standards set by the AI CoE.
- Establish and evolve reference architectures and reusable patterns for GenAI and applied AI (RAG, agents/orchestration, vector search, prompt & tool design, event‑driven microservices, API gateways).
- Select fit‑for‑purpose models and services (e.g., Azure OpenAI, Bedrock, Vertex, OSS LLMs, embedding models), articulating clear tradeoffs across performance, latency, privacy, and cost.
- Partner with product and platform teams to ship production‑grade solutions, guiding teams from prototype → pilot → scaled production.
- Define and enforce best practices for CI/CD, Infrastructure as Code, and MLOps/LLMOps, including model versioning, prompt/config management, evaluation frameworks, drift detection, and safety monitoring.
- Ensure observability and operational readiness (tracing, guardrails, red‑teaming, cost dashboards, SLOs, runbooks) before production cutover.
- Review critical pull requests, architecture decisions, and platform changes to raise overall engineering quality.
- Act as a technical leader and multiplier, coaching engineers and architects on responsible, pragmatic AI adoption.
- Build and mentor a small group of senior architects and technical leads, helping grow the next generation of AI leaders.
- Evangelize effective use of copilots, agent frameworks, and integration SDKs to improve developer velocity without compromising quality or security.
- Raise the bar on engineering excellence through design reviews, threat modeling, coding standards, and documentation discipline.
- Lead architecture discovery with business stakeholders: frame problems, quantify constraints, and translate business goals into technical roadmaps.
- Define and track outcome‑based KPIs (time to first value, cost to serve, task success, accuracy, CSAT/NPS, deflection).
- Communicate architectural tradeoffs, risks, and roadmaps in clear, executive‑ready language.
- Publish and maintain architecture decision records (ADRs) and platform documentation to ensure
Qualifications
Education & Experience
- Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or equivalent practical experience.
- 10+ years in software engineering, solution architecture, or platform engineering, including 3–5+ years delivering applied ML/GenAI solutions in production.
- Demonstrated experience leading architecture across multiple teams or products, not just contributing as an individual architect.
- Extensive hands‑on experience with cloud platforms (GCP preferred), including:
- Vertex AI, BigQuery, Dataflow, Pub/Sub
- Cloud‑native microservices, APIs, event streaming
- Containers and orchestration (Kubernetes/GKE)
- Infrastructure as Code (Terraform)
- Deep practical expertise with GenAI patterns: RAG, vector databases, prompt engineering & evaluation, agent design, function/tool calling, and orchestration.
- Strong command of MLOps/LLMOps, including CI/CD for models and prompts, offline/online evaluation, telemetry, drift detection, and safety monitoring.
Skills & Abilities
- Experience operating in regulated industries (financial services, healthcare, public sector) or similarly high‑trust environments.
- Strong background in security, privacy, and compliance‑by‑design, including OAuth/OIDC, secrets management, data protection, and AI safety controls.
- Proven ability to influence without authority, aligning product, engineering, security, and business stakeholders.
- Exceptional written and verbal communication skills, with demonstrated executive presence.
- Certifications (nice to have): Cloud Architect, Security (e.g., CISSP/CCSK), or equivalent.
Other Requirements:
- Ability to work occasional overtime.
- Occasional travel (up to ~15%).
- Occasional after‑hours work to support releases or incident response.
- Prolonged periods of sitting at a desk and working on a computer.
