Concevoir et prototyper un modèle de reconnaissance vocale spécifique aux communications radio de contrôle aérien, explorer son utilisation métier et rédiger un rapport sur les limites et pistes d’industrialisation.
Responsibilities
Réaliser un état de l'art des modèles de reconnaissance vocale (ASR) et de compréhension du langage (NLU) appliqués aux communications aéronautiques
Constituer et préparer des jeux de données audio (corpus publics) pour l'entraînement et l'évaluation
Affiner un modèle ASR (type Whisper ou wav2vec 2.0)
Évaluer les performances par des indicateurs chiffrés et comparer plusieurs approches
Possibilité de développer un module d’utilisation métier
Requirements
Master 2 en informatique / intelligence artificielle
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.
Locations (2)
Bengaluru, Karnataka, India
New Delhi, Delhi, India
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.
Seeking a highly skilled and experienced Senior Data Scientist with a minimum of 8 - 11 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture.
Responsibilities
Contribute to the design and implementation of state-of-the-art AI solutions.
Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI.
Collaborate with stakeholders to identify business opportunities and define AI project goals.
Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases.
Requirements
Bachelor's degree in Computer Science, Engineering, or a related field
Master's degree in Computer Science, Engineering, or a related field
Internship in AI engineering at Infobip, focusing on AI/ML solutions and production-ready services.
Responsibilities
Build and experiment with AI/ML solutions using Python, Jupyter, and libraries such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers
Develop production-ready services in Python, building scalable and maintainable APIs with FastAPI
Work with large datasets and learn the end‑to‑end flow of data collection, manipulation, and usage
Design and build agentic workflows using LLMs, tools, and APIs
Integrate with leading AI providers for text, image, and multimodal use cases
Requirements
Final‑year student in a relevant field (e.g. Software Engineering, Computer Science, Mathematics, Data Science)
Nice to have: Advanced context engineering practices
Gain practical experience and learn from experienced mentors by working on real, data-driven problems across the full AI engineering lifecycle.
Responsibilities
Build and experiment with AI/ML solutions using Python, Jupyter, and libraries such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers
Develop production-ready services in Python, building scalable and maintainable APIs with FastAPI
Work with large datasets and learn the end-to-end flow of data collection, manipulation, and usage
Design and build agentic workflows using LLMs, tools, and APIs
Integrate with leading AI providers (e.g. OpenAI, Anthropic, Google, open-source models) for text, image, and multimodal use cases
Requirements
Final-year student in a relevant field (e.g. Software Engineering, Computer Science, Mathematics, Data Science)
Gen AI Project Manager responsible for designing, developing, and deploying Custom AI agents using LLMs and multi-modal models, and managing complex generative AI programs.
Responsibilities
Demonstrate advanced programming expertise in Python with TensorFlow, PyTorch, and Hugging Face Transformers
Architect and implement Retrieval-Augmented Generation (RAG) pipelines
Design, develop, and deploy Custom AI agents using LLMs and multi-modal models
Implement and manipulate complex algorithms for generative AI models
Manage data pipelines including pre-processing, augmentation, and synthetic data generation
Designing scalable LLM and NLP solutions for high-volume customer interactions, driving architecture, best practices, and innovation across voice AI systems.
Responsibilities
Architect and develop AI-driven IVR and conversational systems using Python
Build NLP models for text classification and clustering to support intent detection and call routing
Perform data analysis using SQL
Design and deploy LLM workflows (prompt engineering, RAG, evaluation) for voice and chat use cases
Lead development of agentic AI workflows for multi-step customer interactions
Design, evaluate, optimize, and operationalize enterprise AI systems across the complete model lifecycle.
Compensation
Medtronic offers a competitive Salary and flexible Benefits Package
Responsibilities
Design and implement training, fine-tuning, and adaptation pipelines for open-weight Large Language Models using supervised fine-tuning, LoRA, QLoRA, and Parameter-Efficient Fine-Tuning (PEFT) techniques.
Build and curate high-quality datasets from operational data sources, incidents, runbooks, logs, knowledge articles, and validated interactions for model training and evaluation.
Develop model evaluation frameworks covering reasoning, instruction following, hallucination detection, tool calling, safety, robustness, and structured output quality.
Evaluate and optimize retrieval-augmented generation systems through retrieval analysis, embedding model comparison, reranking, chunking strategies, and groundedness assessment.
Design and execute agent and tool-calling benchmarks that measure task completion, tool selection accuracy, failure recovery, and operational safety.
Requirements
Bachelor's Degree in Engineering
MCA
MSc
Nice to have: direct preference optimization (DPO)
