We are looking for a talented Machine Learning Engineer who not only possesses a strong foundation in mathematics and statistics but is also well-versed in software engineering principles. In this role, your primary focus will be tackling complex problems related to time-series data using deep learning approaches and optimization algorithms.

Key Responsibilities

• Analyze, preprocess, and build models for complex time-series data (forecasting, anomaly detection, etc.).
• Design and develop Machine Learning and Deep Learning models with a clear understanding of their underlying mathematical logic.
• Implement and utilize optimization algorithms (heuristic and mathematical) to improve model performance and solve complex system challenges.
• Contribute to software quality by conducting code reviews, optimizing algorithms, and proposing architectural improvements.
• Write clean, scalable, maintainable, and production-ready code in Python.
• Collaborate closely with backend and software development teams to integrate AI models into the company’s software infrastructure.

Job Requirements

Hard Skills:

• Machine Learning & Deep Learning: Mastery of ML/DL algorithms with a deep understanding of their foundational mathematics (Linear Algebra, Calculus, Gradient Descent, etc.).
• Time Series Analysis: Proven hands-on expertise in working with time-series data (familiarity with statistical models like ARIMA, as well as neural networks such as LSTM, Transformers, etc.).
• Optimization Algorithms: Solid understanding of the theory and implementation of optimization algorithms, particularly metaheuristics like Genetic Algorithms (GA), PSO, and other mathematical optimization techniques.
• Software Engineering Fundamentals: Strong grasp of software engineering principles, Object-Oriented Programming (OOP), Design Patterns, and Clean Code. Proficiency with Git and the software development life cycle (SDLC).
• Statistics & Probability: Strong academic and practical knowledge of statistics and probability for analyzing data distributions, hypothesis testing, and understanding model uncertainty.
• Python Programming: Expert-level proficiency in Python for Data Science (NumPy, Pandas, PyTorch/TensorFlow, SciPy) and general software development.

Nice to Have:

• Familiarity with MLOps concepts and model deployment tools (e.g., Docker, CI/CD pipelines, MLflow, FastAPI).
• Experience with time-series databases (e.g., InfluxDB) and relational databases (SQL).
• Published papers or contributions to open-source projects related to optimization or time series.