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Turnitin is seeking an AI Specialist for Revenue Operations to build and operate AI workloads that support the global revenue organization. This is the first dedicated engineering resource within a newly established AI orchestration platform initiative that will transform how commercial teams work through AI agents and automations.
You will own two primary areas: (1) developing automations and AI agent features against defined specifications, including prompt engineering, retrieval systems, and knowledge grounding; and (2) operational ownership of live AI systems—evaluation, knowledge curation, monitoring, diagnosis, and production readiness.
Key responsibilities include:
- Building AI agents, automations, and integrations against written specifications using established platform patterns
- Authoring and running evaluation test cases for AI workloads, extending coverage as systems evolve, and producing performance reporting
- Curating and maintaining knowledge bases, content, and data that ground AI agents; preparing and transforming data for agent consumption
- Monitoring live AI workloads, investigating failures, resolving issues within scope, and escalating with clear technical diagnosis
- Building and maintaining integrations with Revenue Operations systems via APIs and workflow orchestration tooling, with attention to authentication, error handling, and access control
- Supporting movement of AI workloads into production through technical documentation, security reviews, and release/rollback preparation
- Maintaining accurate documentation for all AI workloads and documenting reusable patterns to accelerate future builds
- Contributing to ongoing development of the AI orchestration platform under technical direction of the Senior Director, Revenue Enablement & AI Transformation
- Communicating AI workload behavior and limitations clearly to non-technical colleagues across Revenue Operations and the wider revenue organization
- Gathering user feedback on live AI workloads and applying it to improve agent quality and reliability
You will help establish working practices and standards around documentation, evaluation, and production readiness that will determine how quickly AI platforms scale and how much technical work can be safely devolved over time.
REQUIREMENTS:
Essential:
- Hands-on experience building with large language model APIs, including prompt engineering and retrieval or knowledge base systems (self-directed and personal project work counts)
- Working knowledge of Python or JavaScript, sufficient to read, modify, and extend an existing codebase confidently
- Practical experience integrating with REST APIs, including authentication and handling failure conditions in systems outside your control
- Working knowledge of SQL and relational data structures
- Excellent written communication, particularly in documenting technical findings for mixed audiences
- Legal eligibility for authorized employment within the Philippines region
- Fluent in verbal and written English
Preferred:
- Experience with workflow automation or orchestration platforms (n8n, Make, Zapier)
- Technical exposure to CRM or sales engagement platforms (Salesforce, HubSpot)
- Experience in software testing, quality assurance, or evaluation of AI systems
- Experience in technical support, troubleshooting, or incident diagnosis
- Experience with knowledge management, content curation, or technical documentation
- Familiarity with cloud infrastructure, particularly AWS
- Experience with conversational or voice AI platforms
- Knowledge and experience working within the EdTech sector
Education:
- Degree-level education in a technical discipline or equivalent demonstrable experience. Candidates with a portfolio of self-taught, built work are encouraged to apply.
Proven characteristics for success:
- Demonstrated ability to build working software with AI tooling, evidenced by delivered work rather than formal training alone
- Strong diagnostic instinct and persistence in establishing why a system behaved as it did
- Comfortable working to defined technical direction while taking full ownership of implementation
- Surfaces ambiguity in specifications rather than proceeding on assumption
- Data-driven approach to quality, focused on measurement rather than assertion
- Disciplined about documentation with understanding of why it matters to a small technical team
- Highly organized with action-oriented mindset and strong written communication skills
