This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Senior AI/ML Engineer/ Developer (India Office). We are seeking a highly skilled and motivated Senior AI /ML Developer to join our expanding India team. This role will focus on designing and deploying machine learning and deep learning models, developing conversational AI applications, and implementing end-to-end AI solutions using cloud-native platforms. The ideal candidate will be a collaborative problem solver with deep technical expertise and a passion for innovation.
Job Responsibility:
Design and deploy ML/AI models using CNN, RNN, Seq2Seq, and LLMs
Develop and fine-tune NLP-based solutions and integrate them into chatbot platforms
Build chatbot applications using Microsoft Bot Framework, Dialogflow, or OpenAI APIs
Perform data preprocessing and feature engineering using Python
Deploy AI models and services on cloud platforms such as AWS or Azure
Collaborate closely with cross-functional teams, including Engineering, Product, and DevOps
Explore advanced AI techniques such as Reinforcement Learning with Human Feedback (RLHF), prompt engineering, and other optimization strategies
Participate in Agile development processes, including sprint planning and reviews
Requirements:
Bachelor's or Master’s degree in Machine Learning Engineering, Data Science, or a related field
5–6 years of hands-on experience in ML/AI engineering or data science
Strong proficiency in Python programming
Solid understanding of machine learning algorithms such as KNN, SVM, and Naive Bayes
Experience with NLP, deep learning (CNNs, LLMs), and related frameworks
Proven experience with cloud platforms like AWS or Azure
Prior experience deploying chatbots or conversational AI solutions in production environments
Nice to have:
Familiarity with Microsoft Bot Framework, Dialogflow, and OpenAI APIs
Understanding of MLOps practices including model versioning, monitoring, and lifecycle management
Bonus: Open-source contributions or published AI research