ML Engineer (NLP)

This role is for one of the Weekday's clients

Min Experience: 2 years

Location: Bangalore

JobType: full-time

We 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.