Sr. AI Data Engineer (UK Remote)

Turnitin
Newcastle upon Tyne
3 days ago
Create job alert
Overview

Turnitin, an AI-focused leader in the educational and research sectors, has been at the forefront of promoting academic integrity and innovation for over two decades. We are renowned for our cutting-edge solutions which have been embraced by thousands of academic institutions, corporations, and publishers worldwide.

We offer remote work as a standard arrangement and value diversity, respecting local cultures and individual choices. We are a remote-first employer with an office in Newcastle (UK) and colleagues across the globe. Our team is diverse yet unified by a shared commitment to making a significant impact in education.

AI and data science are integral to our success and ambitious product roadmap. As a Senior AI Data Engineer, you will join a global team of proactive, supportive, and independent professionals delivering sophisticated, well-structured AI and data systems. You’ll help pioneer our next-generation data and AI pipelines to scale our impact and collaborate with different teams to integrate AI and data science across a broad suite of products that enhance learning, teaching, and academic integrity.

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.
  • Technology Evolution: Stay current with emerging tools and methodologies in AI data engineering, bringing recommendations to enhance our AI data infrastructure and capabilities.
Qualifications

Required Qualifications:

  • 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.

Desired Qualifications:

  • 6+ years of experience in data engineering with a focus on AI and machine learning projects.
  • Experience in a technical leadership or mentorship role.
  • Experience in education, EdTech, or academic integrity sectors.
  • Experience using AI coding tools (Cursor, Claude Code, GitHub Copilot) for accelerated development.
  • Familiarity with natural language processing, computer vision, or multimodal AI applications.
  • Experience with data visualization (Streamlit) and data reporting.
Characteristics for Success

As a Senior AI Data Engineer, you should possess:

  • 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
Additional Information

Total Rewards @ Turnitin
Turnitin maintains a Total Rewards package that is competitive within the local job market. Beyond regular pay and bonuses, Turnitin provides generous time off and health and wellness programs that offer choice and flexibility, supporting your well-being. We maintain a remote-centric culture that enables purposeful and accountable work across diverse roles.

Our Mission is to ensure the integrity of global education and meaningfully improve learning outcomes.

Our Values underpin everything we do.

  • Customer Centric - We realize our mission by putting educators and learners at the center of everything we do.
  • Passion for Learning - We seek teammates who are constantly learning and growing and enable that growth.
  • Integrity - Integrity is the heartbeat of Turnitin, shaping our products, interactions, and partnerships.
  • Action & Ownership - We have a bias toward action and empower teammates to make decisions.
  • One Team - We break down silos, collaborate effectively, and celebrate each other’s successes.
  • Global Mindset - We respect local cultures and embrace diversity, thinking globally and acting locally to maximize impact.
Global Benefits
  • Remote First Culture
  • Health Care Coverage*
  • Education Reimbursement*
  • Competitive Paid Time Off
  • 4 Self-Care Days per year
  • National Holidays*
  • 2 Founder Days + Juneteenth Observed
  • Paid Volunteer Time*
  • Charitable contribution match*
  • Monthly Wellness or Home Office Reimbursement/*
  • Access to Modern Health (mental health platform)
  • Parental Leave*
  • Retirement Plan with match/contribution*

* varies by country

Seeing Beyond the Job Ad

At Turnitin, we recognize it’s unrealistic for candidates to fulfill 100% of the criteria in a job ad. We encourage you to apply if you meet the majority of the requirements because we know that skills evolve over time. If you’re willing to learn and evolve alongside us, join our team!

Turnitin, LLC is committed to the policy that all persons have equal access to its programs, facilities and employment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.


#J-18808-Ljbffr

Related Jobs

View all jobs

Sr. AI Data Engineer (UK Remote)

Sr. AI Data Engineer (UK Remote)

Sr. AI Data Engineer (UK Remote)

Sr. AI Data Engineer (UK Remote)

Sr. AI Data Engineer (UK Remote)

Sr. AI Data Engineer (UK Remote)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.