Senior Machine Learning Engineer
Unifonic
India · Remote · Full-time · Remote
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Work mode
Remote
Job type
Full-time
Experience
5+ years
Salary
Not disclosed
Job Description
Proudly voted a Great Place to Work®, we are a dynamic startup in the SaaS space that is revolutionizing the way businesses communicate.
Our team is made up of 500 energetic and passionate Unifones who are dedicated to delivering the best possible experience to 5000+ customer-centric companies.
We pride ourselves on our fun and collaborative work environment, where creativity and new ideas are constantly encouraged.
As shareholders in the business, we’re so much more than a group of passionate communicators.
We are Unifones.
Join our team and be a part of something big! Meet the team! Our Engineering team is responsible for designing, developing, and maintaining the systems and technologies that drive Unifonic ’s solutions.
We work closely with other departments to ensure our products and services meet the needs of our customers.
If you are passionate about technology and are excited about working on cutting-edge communication and engagement solutions, we want you on our team.
As a Senior Machine Learning (AI) Engineer, you will be responsible for designing, developing, and deploying advanced machine learning solutions across various domains, including NLP, Text Classification, RAG, LLMs, Recommender engines, and Anomaly detection.
This role involves end-to-end project ownership, from data preprocessing to the creation of service APIs, and offers opportunities to work on cutting-edge AI technologies.
Help us shape the future of communication by: Leading the end-to-end design, development, and deployment of robust and scalable machine learning solutions, with a strong emphasis on NLP and RAG architectures.
Architecting and implementing RAG systems, combining large language models (LLMs) with robust retrieval mechanisms to improve the accuracy, factual grounding, and interpretability of generated content.
Applying advanced NLP techniques for tasks such as text classification, entity recognition, sentiment analysis, summarization, question answering, and information extraction.
Researching, evaluating, and integrating state-of-the-art NLP models and RAG frameworks (e.g., Transformers, BERT, GPT variants, Vector Databases, Semantic Search).
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