ML Engineer

Job Description: ML Engineer (Time Series)

Job Title: ML Engineer Time Series

Immediate Joiners Preferred

Experience: 3 - 8 Years

Location: Bengaluru (Bengaluru Based Candidates)

About the Role

We are looking for a skilled Machine Learning Engineer with strong experience in Time Series algorithms, Python, and machine learning to join our team in Bengaluru. The ideal candidate will have hands-on experience developing, deploying, and optimizing time series models and will work closely with Data Scientists and other engineering teams to deliver scalable ML solutions.

Key Responsibilities

• Develop, implement, and optimize time series forecasting and machine learning models.
• Work with large and complex datasets to identify trends, patterns, seasonality, and anomalies.
• Apply time series techniques such as ARIMA/SARIMA, Exponential Smoothing, Prophet, regression-based forecasting, and advanced ML/DL approaches.
• Build robust data preprocessing, feature engineering, model training, validation, and evaluation pipelines using Python.
• Collaborate closely with Data Scientists, Data Engineers, and business stakeholders to translate business problems into ML solutions.
• Evaluate model performance using appropriate forecasting and statistical metrics.
• Productionize ML models and contribute to scalable and maintainable ML pipelines.
• Monitor model performance and identify opportunities for model improvement and retraining.
• Write clean, reusable, and well-tested Python code.
• Document methodologies, experiments, model assumptions, and results.

Required Skills

• 3 - 8 years of experience in Machine Learning, Data Science, or a closely related field.
• Strong programming skills in Python.
• Hands-on experience with Time Series analysis and forecasting algorithms.
• Good understanding of:
• Time series decomposition
• Trend and seasonality
• Stationarity
• Autocorrelation and partial autocorrelation
• Forecasting and model validation
• Feature engineering for time-dependent data
• Experience with ML libraries such as Scikit-learn, Pandas, NumPy, Statsmodels, or equivalent.
• Strong understanding of machine learning concepts, model evaluation, and statistical methods.
• Experience working with Data Scientists and translating analytical models into production-ready solutions.
• Strong problem-solving and analytical skills.

Good to Have

• Experience with deep learning for time series, including LSTM, GRU, Transformers, or Temporal Fusion Transformer.
• Exposure to MLOps/model deployment and cloud platforms such as AWS, Azure, or GCP.
• Experience with Docker, Kubernetes, MLflow, Airflow, or similar technologies.
• Knowledge of SQL and data engineering concepts.
• Experience with anomaly detection, demand forecasting, predictive maintenance, or similar time-dependent use cases.
• Exposure to CI/CD and production ML systems.