Ai Ml Engineer

Role & responsibilities

Job Description

High-Level Job Description

1. Essential experience: Forecasting + end-to-end model building

• Proven delivery of capacity/demand/time-series forecasting solutions (e.g., workforce/headcount and capacity planning, volume forecasting, infrastructure utilisation, contact-centre demand).
• Strong hands-on capability to design, build, train, and validate models end-to-end (not GenAI-only), including feature engineering, model training, tuning, and handover for deployment/productionisation.
• Demonstrated depth in relevant forecasting techniques, such as ARIMA/SARIMA, Prophet, XGBoost/LightGBM for time series, LSTM/Temporal CNN, hierarchical forecasting, etc. (approach may vary; rigour and depth are key).

2. Strong machine learning fundamentals

• Solid understanding of core ML concepts (e.g., epochs, loss/error metrics, overfitting, cross-validation).
• Time-series best practices: time-ordered train/test splits, awareness of data leakage, and handling trend/seasonality, missing data, and outliers.

3. Forecast evaluation and operational readiness

• Clear experience using forecasting metrics and validation practices, including MAPE/SMAPE, MAE/RMSE, prediction intervals, backtesting, and monitoring for drift/retraining triggers.
• Ability to translate forecasts into operational decisions (e.g., capacity planning assumptions, scenario modelling, and what-if driver analysis).

4. Technical stack and engineering maturity

• Proficiency with the Python forecasting/ML ecosystem: pandas, numpy, scikit-learn, statsmodels, Prophet, plus PyTorch or TensorFlow where deep learning is applied.
• Production mindset: version control, reproducibility, documentation, and basic familiarity with MLOps practices (even if lightweight).

Mandatory SkillsMLDesirable Skillsforecasting,Python,ML OPS,Pandas,Capacity Planning

Thanks,

Digvijay Sunil

[email protected]