AI/ML Research Engineer
Informa plc (company site, opens in a new tab)2–4 yrsBengaluruHybridDirect career pageEst. 12–20 LPA
Posted 1d ago
Reach people at Informa plc
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Informa plc you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Research, prototype and evaluate NLP methods that extract entities, relations, topics and emerging research areas from scholarly content.
- Build and assess knowledge graphs, ontologies, semantic search and graph-enhanced RAG; create datasets, metrics and reproducible evaluation evidence.
- Deliver decision-grade prototypes, business cases and handover documentation for Engineering, and communicate findings to technical and non-technical audiences.
Minimum Requirements
- Work Experience Required: 2–4 years applying NLP/ML in industry or research, or an MSc with relevant research or internship experience.
- Relevant bachelor’s degree or equivalent experience; must have the right to live and work in India.
- Strong Python and hands-on NLP/ML experience, including frameworks such as Hugging Face Transformers, spaCy or PyTorch; practical knowledge of knowledge graphs/semantic technologies and Cypher or SPARQL.
- Understanding of embeddings, semantic or hybrid retrieval, RAG and model evaluation. Must live within commuting distance of the office and attend in person at least 3 days per week; JD mentions Bengaluru and, elsewhere, Bengaluru or New Delhi.
Ideal Candidate Profile
- Early-career researcher-engineer who can turn research papers and open-source methods into tested prototypes and evidence-backed recommendations.
- Has practical experience with scholarly or scientific text, metadata, knowledge graphs, ontologies or research discovery; scholarly-domain experience is preferred, not stated as mandatory.
- Likely to succeed in a small, senior, research-led team, owning defined experiments and preparing promising work for handover to Engineering.