Fullstack Architect – AI

• Lead the technical design and act as the single design authority for AdVise, a campaign-management platform operating approximately 1,600 Google Ads and Meta accounts for automotive dealerships
• Record architecture decisions in architecture decision records and the shared decision log
• Define the provider-neutral desired-state and delta model and governed write-path contract
• Design precedence, managed-scope and field-ownership models across Salesforce, live platform state, playbooks, locks, compliance rules, inventory and offers
• Design the rules engine, including rule classes, evaluation order, failure handling, import validation, rendering gates and live-campaign sweeps
• Own the operating envelope, including rate limits, polling cadence, workload yield order and full-book-scale headroom
• Define security and identity architecture, including roles, SSO, service identities, privileged-action audit and approval authority
• Design integrations and data contracts for Google Ads, Meta, Salesforce, Snowflake and client offer/incentive data
• Choose the operational store, queues, orchestration approach and AWS platform build-versus-buy split
• Define and validate measurable non-functional targets through load and performance testing
• Design migration architecture, rollback, pilot-wave scorecard, wave planning and acceptance tolerances with the Engineering Manager
• Own AI Harness architecture and governance, including guardrails, human review gates, approved tooling, spend controls and security review
• Design the Release Two runway for AI agents using governed write paths, locks and audit trails
• Lead technical spikes, design reviews and code reviews; review every capability architecture before build
• Participate in steering, prepare milestone sign-off technical content, document findings and escalate blockers within one business day
• Pair with client engineers and prepare documentation and decision records for independent operation and extension
• Collaborate with product leaders, paid-media strategists, client engineering and data teams, Engineering Manager and delivery team

• Senior-level experience as a solutions or software architect on production platforms integrating with external systems at scale
• Experience designing transactional, auditable write paths, including idempotency, partial failure, retries, reconciliation and concurrency control
• Strong understanding of asynchronous and event-driven architectures, queues, workflow orchestration and scheduled execution
• Experience integrating with rate-limited third-party APIs, including quota budgeting, polling strategies and change-data capture
• Experience with relational data modelling, SQL, data contracts, and integration with CRM platforms and data warehouses
• Strong grounding in security architecture: authentication, authorization, role-based access, service identities, secret management and audit
• Cloud architecture experience on AWS, or deep experience on another major cloud platform and ability to ramp quickly on AWS
• Understanding of infrastructure as code, CI/CD, environment separation, observability and production support
• Working knowledge of Vue.js and PHP and Laravel, sufficient to review designs and code produced in those stacks
• Experience writing architecture decision records and technical documentation for inheriting teams
• Experience delivering inside fixed-price, milestone-based engagements, including scope control and acceptance evidence
• Strong communication skills and ability to explain technical decisions and trade-offs to executives and engineers
• Practical experience designing AI-assisted engineering workflows or AI harnesses with human review gates
• Ability to decide when a capability should be deterministic, AI-assisted, or agentic, and defend that boundary
• Understanding of AI guardrails covering permissions, tool access, prompt injection, data leakage, audit trails, evaluation and token cost
• Experience designing architectures in which AI agents act through the same governed paths, approvals and audit trails as human users
• Familiarity with structured outputs, tool calling, context management, model selection, and managed model access inside a client's own cloud account
• Ability to establish a secure and reusable AI platform foundation before introducing autonomous capabilities
• Preferred: Experience with the Google Ads API, Meta Marketing API, or similar advertising platforms
• Preferred: Experience with Salesforce, Snowflake, Datadog, or Looker
• Preferred: Experience migrating live production systems onto a new platform without service interruption
• Preferred: Experience with campaign management, marketing technology, compliance engines, or rules-based systems
• Preferred: Familiarity with automotive retail, OEM advertising programs or co-op compliance
• Preferred: Cloud architecture certifications on AWS, Azure or GCP
• Preferred: Experience leading knowledge transfer and platform handover to a client's engineering team