Informa
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We are seeking an AI/ML Research Engineer (NLP & Knowledge Graphs) to join our Applied AI Solutions team.
Taylor & Francis is one of the world’s leading publishers of scholarly journals, books, eBooks and reference works. We are creating technology platforms to shape the future of knowledge.
Taylor & Francis Group produces high quality, peer reviewed books and journals. We produce unique, trusted content by expert authors, spreading knowledge and promoting discovery globally. We aim to broaden thinking and advance understanding, providing academics and professionals with a platform to share ideas and realise their individual potential.
As part of Taylor & Francis’s digital first strategy, we have been driving growth through providing access to publications through digital platforms and providing a unified experience across the body of content. We have created digital platforms to facilitate discovery and to allow users to access relevant research and information quickly and easily, wherever they are.
Taylor & Francis is building the world's leading platform connecting the network of human knowledge. We are passionate about helping researchers achieve their goals, which in turn adds new nodes to the ever-expanding graph of research information. We believe that researchers are our number one priority, so we work hard to deliver value back to them with everything we do.
Our teams are solving unique and difficult problems at scale in an energetic and hyper-growth environment. We’re growing incredibly quickly, and we need experienced technical people to help us scale.
We are seeking an AI/ML Research Engineer (NLP & Knowledge Graphs) to join our Applied AI Solutions team. This is a junior-to-mid level role: the ideal candidate has around 2–4 years of hands-on experience applying NLP and machine learning (or an MSc with strong research or internship experience), and wants to grow into a research-minded engineer who can take an idea from paper to working prototype.
You will research, prototype and evaluate ways of extracting structure and meaning from scholarly content – entities, relations, topics and emerging research areas – and of representing it in knowledge graphs and ontologies that can power better discovery, search and recommendation across Taylor & Francis. You will report to the Director of Applied AI Solutions, who in turn reports to the CTO.
The Applied AI Solutions team is a small, senior, research-led group within the CTO organisation. We are the part of Taylor & Francis that works on problems that are on nobody’s product roadmap yet – discovery, peer review, research integrity and making scholarly content machine-consumable – and we turn the most promising ideas into evidence, working prototypes and business cases that Engineering and the business can adopt.
Work as part of a small, research-led team on a few strategic AI “bets” each year, each ending in a decision-grade artefact: a working prototype, the evidence behind it and a business case.
Research, prototype and evaluate NLP approaches for extracting entities, relations and topics from scholarly articles, books and metadata.
Help design, populate and evolve knowledge graphs, ontologies and taxonomies that represent Taylor & Francis content and the research landscape around it.
Build and benchmark semantic and hybrid retrieval approaches, including embedding models and graph-enhanced retrieval-augmented generation (RAG).
Contribute to the team’s technology radar by scouting, trialling and writing up new open-source libraries, models and tools for knowledge extraction and search.
Build test sets, metrics and evaluation harnesses so that every claim we make about a model or tool is backed by evidence.
Support the hand-over of graduating prototypes to Engineering teams, with clear documentation and a written definition of what “done” means.
Present results clearly to technical and non-technical audiences, and contribute to papers, articles and conference submissions.
Develop NLP pipelines for tasks such as named-entity recognition, relation extraction, entity linking and disambiguation, topic modelling and classification over scholarly text, using transformer models and LLMs.
Build, populate and query knowledge graphs in a graph database or triple store (e.g., Neo4j, RDF/SPARQL stores) and evaluate their quality and coverage.
Support the design and expansion of ontologies, taxonomies and controlled vocabularies, working with Editorial and subject-matter experts.
Detect and track emerging topics and research areas using clustering, topic modelling and embedding-based techniques.
Evaluate embedding models, chunking strategies, rerankers and retrieval configurations for semantic search and RAG on scholarly content.
Create gold-standard datasets and annotation guidelines, and define metrics (e.g., precision/recall/F1, nDCG) for reproducible evaluation.
Design and run well-controlled experiments, tracking them with tools such as MLflow or Weights & Biases so results can be reproduced.
Prototype rapidly using LLM APIs and open-source models for knowledge extraction and enrichment.
Write clean, tested and documented Python code that other engineers can pick up and extend.
Prepare hand-over documentation and support Engineering teams during the build phase of prototypes that graduate to production.
Collaborate with Engineering, Product, Editorial and other business teams to turn open-ended problems into well-posed research questions.
Assist with vendor, model and tool comparisons, including preparing benchmark results for internal and external audiences.
Keep up with the research literature and relevant venues (e.g., ACL, EMNLP, SIGIR, ISWC/ESWC) and share what is relevant with the team.
Good to have certifications
Neo4j Certified Professional (or equivalent graph database certification)
DeepLearning.AI or Hugging Face NLP / LLM specialisation certificates
Cloud ML or AI certification (e.g., AWS Machine Learning, Microsoft Azure AI Engineer)
2–4 years of experience applying NLP and machine learning in industry or research (or an MSc with relevant research or internship experience).
Strong Python, including scientific libraries (pandas, numpy, scikit-learn) and writing readable, tested code.
Hands-on experience with NLP libraries and frameworks such as spaCy, Hugging Face Transformers and PyTorch.
Practical knowledge of knowledge graphs and semantic technologies: property graphs and/or RDF, OWL and SKOS, with query experience in Cypher and/or SPARQL.
Understanding of information retrieval and semantic search: embeddings, vector search (e.g., FAISS, OpenSearch/Elasticsearch, pgvector), hybrid retrieval and RAG.
Experience designing evaluation: test sets, annotation, metrics, error analysis and statistical sanity-checking of results.
Working familiarity with LLM APIs and structured-output prompting for information extraction.
Experience with Git, Linux, SQL and reproducible experiment environments (notebooks, virtual environments, containers).
Ability to read research papers and reproduce their results from the paper or open-source repository.
Experience with a cloud platform (AWS preferred) is a plus.
Curious, rigorous and sceptical: comfortable saying “the evidence does not support this” and showing why.
Clear written and spoken communication, including explaining technical results to non-technical colleagues.
Comfortable working under the technical direction of senior engineers while taking ownership of well-defined pieces of work.
Able to manage several experiments at once and prioritise in a fast-paced environment.
A keen eye for detail and a commitment to reproducible, high-quality work.
Experience with scholarly or scientific text and metadata (e.g., Crossref, OpenAlex, ORCID, ROR, PubMed).
Exposure to ontology and taxonomy tooling (e.g., Protégé, PoolParty, SKOS editors).
Exposure to LLM fine-tuning (e.g., LoRA, PEFT) or agentic frameworks (e.g., LangGraph).
Prior publication, conference presentation or open-source contribution.
Familiarity with Agile methodologies and project management tools (e.g., Jira).
Bachelor’s degree in computer science, data science, computational linguistics, mathematics or a related field (or equivalent experience). Master’s degree (or PhD in progress/completed) is an advantage but not required.
You must have the right to work and live in India.
Location: The successful candidate will be able to utilise our balanced working model. We ask that candidates live within a commutable distance of our Bengaluru office to ensure that as a balanced worker they can make it to our office location to collaborate as required. There is an expectation for a minimum of 3 days per week in-person time.
We believe that great things happen when people connect face-to-face. That's why we work in-person with each other, or with customers and partners, three days a week or more. When you’re not spending time together in one of our offices or other workplaces – like at conference – you get the flexibility and support to work from home or remotely.
An excellent Work/Life balance with a fantastic flexible working culture
25 days annual leave
3 additional discretionary days off during the holiday season at the end of the year
4 paid Volunteering days each year
Day off for your birthday
Pension contributions
Seasonal social and charitable events
Training and development
We believe that great things happen when people connect face-to-face. That's why we work in-person with each other, or with customers and partners, three days a week or more. When you’re not spending time together in one of our offices or other workplaces – like at conference – you get the flexibility and support to work from home or remotely. The successful candidate will be able to utilise our balanced working model. We ask that candidates live within a commutable distance of either our Bengaluru or New Delhi office location to ensure that as a balanced worker they can make it to our office locations to collaborate as required. There is an expectation for a minimum of 3 days per week in-person time.
At Taylor & Francis we care about our colleagues, promoting work-life balance, wellbeing and flexible working. We believe that the skills and experience you bring to Taylor & Francis are invaluable. We want you to have the opportunity to develop your abilities, and to innovate and develop in areas which you are passionate about.
We know that sometimes the 'perfect candidate' doesn't exist, and that people can be put off applying for a job if they don't fit all the requirements. If you're excited about working for us and have most of the skills or experience we're looking for, please go ahead and apply. You could be just what we need! We believe strongly in the value of diversity and creating supportive, inclusive environments where our colleagues can succeed. As such, Informa is proud to be an Equal Opportunity Employer. We do not discriminate on the basis of race, colour, ancestry, national origin, religion, or religious creed, mental or physical disability, medical condition, genetic information, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender identity, gender expression, age, marital status, citizenship, or other protected characteristics under local law.
To find out more about our business and the great career opportunities please go to our Careers Site: http://www.taylorandfrancisgroup.com/careers
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