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.