Machine Learning Engineer

Machine Learning Engineer

Location: Bangalore

Experience:2–4 Years

Employment Type: Full-time

About the Role

We are looking for a Machine Learning Engineer to design, develop, and deploy advanced machine learning systems focused on forecasting, optimization, and AI-driven solutions.

The ideal candidate will have a strong foundation in Mathematics, Statistical Modeling, Machine Learning, and Large Language Models (LLMs), with the ability to translate complex problems into scalable, production-ready solutions.

Key Responsibilities

• Design and implement end-to-end ML pipelines for production environments.
• Develop forecasting and optimization models using advanced mathematical and statistical techniques.
• Build, pre-train, fine-tune, and evaluate ML and LLM-based models.
• Apply strong knowledge of probability, statistics, linear algebra, calculus, and optimization to solve complex problems.
• Develop and integrate LLM and Generative AI solutions into production workflows.
• Conduct structured experimentation, model validation, and performance optimization.
• Work with large-scale and real-time datasets to build predictive systems.
• Collaborate with cross-functional teams to integrate ML models into live workflows.
• Build scalable and low-latency ML infrastructure.
• Maintain technical documentation for reproducibility and maintainability.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Statistics, Data Science, or a related field.
• 2–4 years of hands-on experience in developing and deploying ML models in production.
• Strong proficiency in Python.
• Experience with PyTorch, TensorFlow, and scikit-learn.
• Strong foundation in Mathematics, including probability, statistics, linear algebra, calculus, optimization, and mathematical modeling.
• Good understanding of time-series forecasting, statistical learning, predictive modeling, and model evaluation.
• Hands-on exposure to LLMs, Generative AI, NLP, fine-tuning, prompt engineering, or LLM evaluation.
• Experience with Git, Docker, and Kubernetes.
• Strong analytical and problem-solving skills with a focus on experimentation and validation.

Good to Have

• Exposure to Reinforcement Learning.
• Experience in portfolio optimization, quantitative modeling, or signal generation.
• Experience working with real-time or large-scale datasets.
• Knowledge of LLM inference optimization and production deployment.