AI/ML Research Engineer
Taylor & Francis · Bengaluru · onsite · Posted 2d ago
Undisclosed2–4 yrsfulltimeSourced from
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About this role
Job Description
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.
What you ll be doing:
- 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.
What we re looking for:
Key Responsibilities:
- 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.
Qualifications
Qualifications :
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)
Technical Skills:
- 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.
Soft Skills:
- 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.
Preferred:
- Experience with scholarly or scientific text and metadata (e.g., Crossref, OpenAlex, ORCID, ROR, PubMed).
- Exposure to ontology and taxonomy tooling (e.g., Prot g , 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).
Education:
- 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.
This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
Sourced from naukri
Skills
Agileapisclusteringcloud platformamazon web serviceselastic searchclassificationFAISSai engineerInformation RetrievalEmbeddingsAI certificationGraph DatabasecloudAi SolutionsMicrosoft AzureGITData ScienceArtificial IntelligenceEditorial
About Taylor & Francis
Taylor & Francis
Printing & Publishing
5
employees
1852
174 years old
London, England
United Kingdom
₹16.3L PA avg
Avg at Taylor & Francis
About
For more than two centuries, we have been committed to the highest quality scholarly publishing.
We believe in making top quality research accessible. With offices in 14 countries, we are a truly global publishing company.
We have published over 180,000 book titles, and we publish over 2,700 journals in a wide range of disciplines across six imprints and 75 subjects. We are also a leading digital publisher with over 100,000 eBooks available. Our journals platform, Taylor & Francis Online hosts millions of journal articles, and we are one of the world’s largest publishers of open access research. We also partner with more than 700 scholarly societies.
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