Machine Learning Engineer

Machine Learning Engineer
Location: Bangalore
Experience: 2–3 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.
2–3 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.

Work arrangement
No