Senior AI Data Engineer, Turnitin

• At least 4 years of experience in data engineering, ideally focused on AI/ML data infrastructure or enabling and accelerating AI R&D
• 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
• A passion for creatively solving complex data problems
• The ability to work collaboratively and cross-functionally
• A continuous learning mindset, always striving to improve your skills and knowledge
• A proven track record of delivering results and ensuring a high level of quality
• Strong written and verbal communication skills
• 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 a Senior AI Data Engineer 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
• AI Data Infrastructure & Pipeline Management for Applied AI: Design, build, and operate scalable real-time data pipelines that support ongoing Applied AI model training. Deploy and maintain robust data infrastructure using AI techniques and engineering best practices to ensure continuous model improvement and deployment cycles
• Data Collection: Execute initiatives for collecting, normalizing, and storing data across multiple sources, including external LLM providers
• Collaboration: Partner with AI R&D, Applied AI, and Data Platform teams to ensure seamless data flow and quality standards. Partner with stakeholders to collect, curate, and catalog high-quality datasets that directly support Applied AI retraining workflows and business objectives
• AI R&D Support: Provide secondary support to AI Research & Development efforts by applying advanced data warehousing and engineering technologies. Contribute to exploratory data initiatives that uncover insights from Turnitin's extensive data resources
• Communication: Maintain clear communication channels across teams, ensuring alignment with company vision while sharing insights on data infrastructure needs and potential innovations
• Technology Evolution: Stay current with emerging tools and methodologies in AI data engineering, bringing recommendations to enhance our AI data infrastructure and capabilities