About the role
This role is for one of the Weekday's clientsMin Experience: 2 yearsLocation: BangaloreJobType: full-timeWe are looking for a Machine Learning Engineer (NLP) who is passionate about solving complex problems, working with massive datasets, and transforming theoretical concepts into scalable, real-world solutions. You thrive in a collaborative yet autonomous environment where your ideas can make a significant impact. You are excited about leveraging Machine Learning, Natural Language Processing (NLP), Information Retrieval, and related AI technologies to push boundaries and drive innovation.
Requirements
Key Responsibilities
Design, develop, and deploy machine learning models for NLP, retrieval, ranking, reasoning, dialog, and code-generation systems.
Implement advanced ML algorithms, including Transformer-based models, reinforcement learning, ensemble learning, and agent-based systems, to enhance AI performance.
Process and analyze large, complex datasets (structured, semi-structured, and unstructured) to inform model development.
Own the end-to-end ML model lifecycle, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
Conduct A/B testing and apply statistical methods to validate model effectiveness.
Develop automated testing and validation processes to ensure model integrity and robustness.
Collaborate with both technical and non-technical stakeholders to clearly communicate model insights and benefits.
Qualifications & Skills
Education & Experience
Master's or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
Proven industry experience in developing and deploying production-grade ML models.
Technical Skills
Strong expertise in Natural Language Processing (NLP), including training and inference of large language models.
Deep understanding of retrieval, ranking, reinforcement learning, and agent-based systems, with experience in building large-scale implementations.
Proficiency in Python and hands-on experience with ML libraries such as TensorFlow or PyTorch.
Strong data processing skills (SQL, ETL, data warehousing) and experience working with large-scale data systems.
Familiarity with MLOps principles, ML lifecycle management tools, and cloud platforms like GCP or Azure.
Up-to-date knowledge of machine learning research trends and the ability to apply them in practical applications.
Solid understanding of software development principles, data structures, and algorithms.
Soft Skills
Problem-solving mindset, strong attention to detail, and logical thinking.
Ability to work collaboratively in a fast-paced startup environment while taking ownership of key initiatives.
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