Benefits:

• Remote First Culture

• Health Care Coverage*

• Education Reimbursement*

• Competitive Paid Time Off

• 4 Self-Care Days per year

Experience Requirements:

• 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)

Other Requirements:

• Strong problem-solving, analytical, and communication skills, with the ability to design scalable AI data systems and collaborate effectively in cross-functional teams

Responsibilities:

• 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.