[Hiring] AI Architect @The Information

Role Description

We're looking for an AI Architect to lead the design, evolution, and scaling of our AI pipeline infrastructure. You'll take ownership of a backend platform built to orchestrate AI workflows at scale, and extend it to support a growing set of high-fan-out AI features that are central to our product strategy.

What You'll Inherit

• An internal, API-only backend application deployed on a cloud PaaS (staging and production environments)
• A background job processing system with multiple queues and a durable message broker
• A dedicated relational database for pipeline state and history
• A job base class/pattern that provides idempotency guards, status-transition state machines, retry-with-backoff, structured logging, and error reporting on failure
• Authenticated API access for inbound integrations, with secure credential management for outbound integrations
• Multiple LLM providers for generation, structured output, and embeddings
• A vector database for similarity search and matching at scale
• Integrations with adjacent internal systems (content/CMS, notifications, messaging/chat tooling, tracing and error-monitoring platforms)

What you'll do

• Own and Operate (Immediate)
  • Maintain and operate all existing AI pipelines running on the platform
  • Complete the migration of remaining workflows from the legacy orchestration framework to the primary platform
  • Own on-call response for AI pipeline failures, including failed-job triage and retries
  • Manage LLM provider relationships, API key rotation, cost tracking, and model upgrades
• Architect and Scale (Ongoing)
  • Design and implement new high-fan-out AI pipelines on the existing platform — built to support future horizontal workflows (many generations across many users/items) without significant rework
  • Establish patterns and conventions for onboarding new pipelines, including registry entries, job subclassing, batch fan-out, and automated reporting
  • Drive architectural decisions around data storage strategy, queue partitioning, concurrency throttling, and cost controls as pipeline volume grows
  • Evaluate and integrate new LLM providers and embedding models as the landscape evolves, while maintaining backward compatibility with existing vector data
  • Build observability and operational tooling — extending the primary ops dashboard with custom reporting, cost tracking, and alerting as needed
• Collaborate and Lead
  • Partner with product, editorial/content, and growth teams to translate product requirements into pipeline designs
  • Work across the engineering organization to integrate AI pipelines with the broader technical stack
  • Mentor engineers on AI pipeline patterns, prompt engineering, and platform architecture
  • Own the technical proposal process for new pipelines and major infrastructure changes

Qualifications

• Deep backend framework expertise — 5+ years building production applications in a modern web framework (e.g., Rails, Django, or similar), with strong experience in ORM usage, API-only application design, and background job processing
• Job orchestration at scale — Proven experience designing idempotent, retryable, fan-out job pipelines (batching, concurrency controls, dead-letter handling, queue partitioning)
• LLM integration experience — Hands-on work with multiple LLM providers, structured output, embeddings, prompt engineering, and cost tracking
• Vector search infrastructure — Experience with a vector database for similarity matching at scale (batched queries, namespace management, embedding model migration)
• Production operations mindset — Experience running internal services on a cloud PaaS or equivalent, with relational databases, caching/queueing infrastructure, and observability tooling
• Architecture and proposal-driven development — Track record of authoring technical design documents, making build-vs-buy decisions, and designing platforms meant to be extended by others

Preferred Qualifications

• 5+ Years of Ruby/Ruby on Rails experience
• Experience migrating workflows from a Python-based orchestration framework to Ruby-based framework
• Familiarity with agent orchestration and LLM tracing/observability ecosystems
• Experience with AI alerting or notification systems (change detection, embedding-based matching, threshold tuning)
• Background in media/publishing AI applications (personalization, content recommendation, cohort-based delivery)
• Experience with state machine libraries for managing job lifecycles
• Experience building retrieval-augmented generation (RAG) pipelines - grounding LLM outputs in retrieved documents or context, not just retrieval-based matching/ranking

What Success Looks Like

• First 90 days: All existing pipelines are stable and well-understood. Remaining migrations are scoped and underway. You've shipped at least one new pipeline or major platform improvement.
• First year: The platform is the single home for all AI orchestration. New pipelines are onboarded in days, not sprints. LLM costs are tracked and optimized. The team trusts the platform's reliability and observability.

Benefits

• Company-paid medical, dental, and vision coverage for employees and their dependents
• Medical coverage that includes fertility care and $0 copays for in-office mental health visits with in-network providers
• Paid parental leave to support and empower new parents
• Generous paid time off (PTO) that increases with tenure
• 401(k) plan with employer matching contributions
• Flexible Spending Accounts (FSAs) for healthcare and dependent care expenses
• Fitness and wellness stipend to encourage a healthy lifestyle
• Monthly cell phone reimbursement
• Company-sponsored lunches in the office every Monday
• Commuter benefits
• A supportive, inclusive, and diverse work environment with a zero-tolerance policy for harassment