Associate ML Scientist (LLM) - II

This is a remote position.

• As an Associate Machine Learning Scientist, you will build machine learning solutions to problems of societal importance under the guidance of senior ML scientists. You will participate in understanding stakeholder requirements, problem definition, model development, evaluation, and deployment of AI solutions, with a particular focus on LLMs and foundation models.
  
• To apply machine learning and LLM technologies for social good, you will need to understand user challenges and their context, curate and transform data, adapt and evaluate models, design meaningful evaluation benchmarks, conduct experiments, and derive insights from data. You will contribute to building reliable, scalable AI systems and work closely with engineering, solutions, domain experts, and designers to ensure that solutions are technically sound and practically useful. You will also interact with social sector organisations and stakeholders to understand real-world needs and help ensure successful deployment of AI solutions.
  
• You will be encouraged to stay current with advances in LLMs and foundation models, explore new techniques where appropriate, and contribute to the continuous improvement of the team's technical capabilities. Excellence as an individual contributor goes together with strong collaboration, curiosity, and a willingness to both learn from and share knowledge, ideas, and emerging practices with the team.
  

Requirements

• Passion for applying AI and machine learning to solve large societal challenges.
  
• Bachelor's or Master's degree (or equivalent) in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or another quantitative discipline, with 1–4 years of relevant work or research experience.
  
• Minimum 2 years of hands-on experience developing and applying machine learning models in an industry or research setting.
  
• Demonstrated experience working with Large Language Models (LLMs) or foundation models through industry projects, research, or significant open-source contributions.
  
• Strong understanding of deep learning fundamentals, including Transformer architectures, attention mechanisms, and modern representation learning techniques.
  
• Experience with one or more LLM adaptation techniques such as supervised fine-tuning, parameter-efficient fine-tuning (e.g., LoRA/QLoRA), instruction tuning, preference optimization, retrieval-augmented systems, structured generation, or tool-using agents. Experience should extend beyond prompt engineering alone.
  
• Experience designing and implementing evaluation methodologies for LLM systems, including benchmarking, model analysis, and iterative experimentation.
  
• Solid software engineering skills in Python and experience with modern ML frameworks such as PyTorch, Hugging Face Transformers, or equivalent.
  
• Experience building end-to-end ML systems, including data preparation, experimentation, model training, evaluation, and deployment.
  
• Track record of applied machine learning projects addressing real-world problems. Experience applying LLMs to practical use cases is highly desirable.
  
• Experience building and evaluating conversational or multi-turn AI systems is a plus.
  
• Excellent written and verbal communication skills and the ability to work effectively in cross-functional teams.
  

Benefits

• Insurance Benefits - Medical Insurance, OPD Coverage, Accidental Insurance, Life Insurance.
  
• Wellness Program - Employee Assistance Program, Annual Health Check-Ups, Onsite Medical Centers, Emergency Support System.
  
• Parental Support - Maternity Benefit, Travel Reimbursement for Expectant Mothers during Last Trimester, Paternity Benefit, Day Care Support Program
  
• Mobility Benefits - Travel Policy, Relocation and Deputation benefits,
  
• Statutory Benefits - Employee PF Contribution, Gratuity, Leave Encashment
  
• Other Benefits – Unlimited Leave Policy, Professional Development Policy, Referral Bonuses, Internet and Telephone Allowance.