Senior Fullstack Architect (AI) - GP, Remote: Colombia - Costa Rica, Fulltime

• This position is open to candidates located in Colombia or Costa Rica only -

We are looking for a Solutions Architect to lead the technical design of AdVise, a campaign-management platform used to operate approximately 1,600 Google Ads and Meta accounts for automotive dealerships. The role owns the architecture every capability depends on: the governed write path, the desired-state model, the precedence rules between systems, the permission model and the platform's operating envelope. The architect acts as the project's single design authority, works hands-on with the engineering team through the build, and leaves the client able to run and extend the platform after handover.

This architect will also own the architecture and governance of the project's AI Harness, working with the Senior Fullstack Engineer who leads its implementation, and will make sure Release One anticipates the prescriptive and agentic capabilities planned for Release Two.

Responsibilities

• Act as the single design authority for the platform, recording each decision in architecture decision records and the shared decision log.

• Define the provider-neutral desired-state and delta model and the governed write-path contract: validation, dry runs, execution, receipts, partial-failure handling, non-bypassable locks, and a live-state read before every write.

• Design the precedence model that decides which source wins when Salesforce, live platform state, playbooks, locks, compliance rules, inventory and offers disagree, together with the managed-scope and field-ownership model.

• Design the rules engine: rule classes, evaluation order, failure handling, validation on import, the forward gate at render and the backward sweep across live campaigns.

• Own the operating envelope: Google Ads and Meta rate limits, change-history polling cadence, a yield order between workloads, and headroom validated at full-book scale.

• Define the security and identity model, including roles, single sign-on, named service identities for every automated actor, privileged-action audit, and the approval authority matrix.

• Design the integration architecture and data contracts for Google Ads, Meta, Salesforce, Snowflake and the client's offer and incentive data, working with the client's data team.

• Choose the operational store, queueing and orchestration approach, and the build-versus-buy split for platform components on AWS, with the client's long-term maintainability in mind.

• Turn scale, latency, recovery and alert-volume expectations into measurable non-functional targets, and validate them through load and performance testing.

• Design the migration architecture with the Engineering Manager: adopt-in-place mechanics, rollback, the pilot wave scorecard, wave planning and acceptance tolerances.

• Own the architecture and governance of the AI Harness used to deliver the platform: guardrails, human review gates, approved tooling, spend controls and the client's security review.

• Design the Release Two runway, so that future AI agents carry their own identities and propose changes through the same governed write path, locks and audit trail as people do.

• Lead technical spikes, design reviews and code reviews, and review the architecture of every capability before its build starts.

• Take part in steering, write the technical content of milestone sign-off packages, document findings with proposed dispositions, and escalate technical blockers within one business day. Pair with the client's engineers throughout the build and prepare the documentation and decision records they need to operate and extend the platform independently.

• Collaborate with product leaders, paid-media strategists, the client's engineering and data teams, the Engineering Manager and the delivery team to translate complex requirements into a maintainable design.

Technical Requirements

• Senior-level experience as a solutions or software architect on production platforms that integrate 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 the 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 teams that will inherit a system.

• Experience delivering inside fixed-price, milestone-based engagements, including scope control and acceptance evidence.

• Strong communication skills and the ability to explain technical decisions and trade-offs to executives and engineers.

AI and Platform Experience

• 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 to 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 Qualifications

• Experience with the Google Ads API, the Meta Marketing API, or similar advertising platforms.

• Experience with Salesforce, Snowflake, Datadog, or Looker.

• Experience migrating live production systems onto a new platform without service interruption.

• Experience with campaign management, marketing technology, compliance engines, or rules- based systems.

• Familiarity with automotive retail, OEM advertising programs or co-op compliance.

• Cloud architecture certifications on AWS, Azure or GCP.

• Experience leading knowledge transfer and platform handover to a client's engineering team.

Originally posted on Himalayas