Manager of AI Data Engineering, Turnitin

• At least 5 years of experience in data engineering and data science, ideally focused on enabling and accelerating AI R&D
• At least 2 years of experience in a managerial or technical leadership role, with responsibility covering large cross-functional projects
• Strong proficiency in Python, SQL, and Infrastructure as Code (Terraform, CloudFormation), with additional experience in modern orchestration frameworks (Airflow, Prefect, or dbt)
• Proficiency with cloud-native data platforms (AWS, Azure, GCP) and vector databases (Pinecone, Weaviate, Qdrant, or Chroma)
• Experience with MLOps tools and platforms (HuggingFace, SageMaker Bedrock, Vertex AI), experiment tracking (MLflow, Weights & Biases), and model deployment pipelines
• Experience with Large Language Models (LLMs), embedding generation, retrieval-augmented generation (RAG) systems, and frameworks for orchestrating LLM interaction (LiteLLM, LangFuse, LangChain, LlamaIndex)
• Strong problem-solving, analytical, and communication skills, with the ability to design scalable AI data systems and collaborate effectively in cross-functional teams
• As an AI Data Engineering Manager, you should have:
• A passion for creatively solving complex data problems
• The ability to work collaboratively and cross-functionally, with strong leadership skills
• A strong growth mindset and ability to constantly improve your skills and knowledge
• Excellent track record of delivering results and ensuring a high level of quality
• A desire to mentor, guide and grow high potential individual contributors
• Excellent written and verbal communication skills
• A sense of curiosity about the problems at hand, the field at large, and the best solutions
• Strong system-level problem-solving skills

Desirable

• AI and data science are integral to our success and ambitious product roadmap, and great AI begins with great data
• Joining us as an AI Data Engineering Manager means you'll become part of a global team of proactive, supportive, and independent professionals committed to delivering sophisticated, well-structured AI and data systems
• You’ll help pioneer our next generation data and AI pipelines to scale our team’s impact
• Additionally, you'll collaborate with different teams within Turnitin to integrate AI and data science across a broad suite of products, designed to enhance learning, teaching, and academic integrity
• In your role as AI Data Engineering Manager, you’ll lead a team that owns all data collection and data curation for the AI team
• There will be a particular emphasis on automated data collection and labeling using state of the art AI and LLMs
• Creativity, collaboration, and a strong problem solving mindset will be critical to your success
• Leadership: Build and grow a team of AI data engineers, ensuring their growth and high performance
• Strategy: Serve as a thought leader in data engineering, advising senior leadership on how to leverage AI-driven data engineering to create future-ready data and AI strategies
• Communication: Ensure clarity of the company's vision and mission across the team and foster excellent communication within the organization
• AI Data Engineering: Design, build, operate and deploy real-time data pipelines at scale using AI methods and best practices. Apply cutting edge data warehousing, data science and data engineering technologies to accelerate Turnitin’s AI R&D efforts. Identify strategic unlocks such as LLM agents to enable a faster time-to-market and better reusability of new AI initiatives
• Collaboration: Cross-functionally partner with teams from across Turnitin and especially the AI R&D, Applied AI, and Data Platform teams to collect, create, curate and catalog high-quality AI datasets that drive our AI pipeline and help answer critical business questions. Ensure alignment and integration of data architecture and data models across different products and platforms
• Hands-on Involvement: Engage in data engineering and data science tasks as required to support the team. Lead external data collection efforts - including state of the art prompt engineering techniques - to support the construction of state of the art AI models
• Innovation: Drive data innovation through research and development to unearth insights from Turnitin's rich data resources
• Continuous Learning: Stay updated on new tools and development strategies in a rapidly evolving technical space, and bring innovation recommendations to leadership