Machine Learning Engineer - LLM

Machine Learning Engineers

This position is ideal for engineers who have excelled in competitive machine learning settings such as Kaggle, possess deep modelling intuition, and can translate complex real-world problem statements into robust, well-structured ML pipelines and datasets. You will work closely with researchers and engineers to develop realistic ML problems, ensure dataset quality, and drive reproducible, high-impact experimentation.

Candidates should have 3+ years of applied ML experience or a strong record in competitive ML, and must be based in India.

Responsibilities

• Frame unique ML problems for enhancing ML capabilities of LLMs.
• Design, build, and optimise machine learning models for classification, prediction, NLP, recommendation, or generative tasks.
• Run rapid experimentation cycles, evaluate model performance, and iterate continuously.
• Conduct advanced feature engineering and data preprocessing.
• Implement adversarial testing, model robustness checks, and bias evaluations.
• Fine-tune, evaluate, and deploy transformer-based models where necessary.
• Maintain clear documentation of datasets, experiments, and model decisions.
• Stay updated on the latest ML research, tools, and techniques to push modelling capabilities forward.

Required Qualifications

• At least

  3 years

  of full-time experience in machine learning model development
• Technical degree in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related field
• Demonstrated competitive machine learning experience (Kaggle, DrivenData, or equivalent)
• Evidence of top-tier performance in ML competitions (Kaggle medals, finalist placements, leaderboard rankings)
• Strong proficiency in

  Python

  ,

  PyTorch/TensorFlow

  , and modern ML/NLP frameworks
• Solid understanding of ML fundamentals: statistics, optimisation, model evaluation, architectures
• Experience with distributed training, ML pipelines, and experiment tracking
• Strong problem-solving skills and algorithmic thinking
• Experience working with cloud environments (AWS/GCP/Azure)
• Exceptional analytical, communication, and interpersonal skills
• Ability to clearly explain modelling decisions, tradeoffs, and evaluation results
• Fluency in English

Preferred / Nice to Have

• Kaggle

  Grandmaster

  ,

  Master

  , or multiple

  Gold Medals

• Experience creating benchmarks, evaluations, or ML challenge problems
• Background in generative models, LLMs, or multimodal learning
• Experience with large-scale distributed training
• Prior experience in AI research, ML platforms, or infrastructure teams
• Contributions to technical blogs, open-source projects, or research publications
• Prior mentorship or technical leadership experience
• Published research papers (conference or journal)
• Experience with LLM fine-tuning, vector databases, or generative AI workflows
• Familiarity with MLOps tools: Weights & Biases, MLflow, Airflow, Docker, etc.
• Experience optimising inference performance and deploying models at scale

Why Join

• Gain exposure to cutting-edge AI research workflows, collaborating closely with data scientists, ML engineers, and research leaders shaping next-generation AI systems.
• Work on high-impact machine learning challenges while experimenting with advanced modelling strategies, new analytical methods, and competition-grade validation techniques.
• Collaborate with world-class AI labs and technical teams operating at the frontier of forecasting, experimentation, tabular ML, and multimodal analytics.
• Flexible engagement options (

  30–40 hrs/week or full-time

  ) — ideal for ML engineers eager to apply Kaggle-level problem solving to real-world, production-grade AI systems.
• Fully remote and globally flexible — optimised for deep technical work, async collaboration, and high-output research environments.