Ai Ml Engineer

Coforge; Pune District; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Coforge is seeking a skilled forecasting specialist to join our analytics team in India. You will lead end-to-end forecasting projects, building and validating models for capacity planning, demand, and time-series scenarios using Python and ML tools.

You will work with Prophet, XGBoost/LightGBM, and deep learning where appropriate, ensuring production readiness with proper validation, monitoring, and documentation. This role emphasizes rigour and scalable ML practices.

Qualifications

• Experience in forecasting demand, capacity and time-series solutions.
• Design, train and validate end-to-end forecasting models with feature engineering.
• Strong ML fundamentals: epochs, loss metrics, overfitting, cross-validation.
• Time-series best practices: time-ordered train/test splits, data leakage, trend/seasonality, missing data, outliers.

Responsibilities

• Develop and deploy forecasting models for capacity planning and demand forecasting.
• Evaluate models using MAPE/SMAPE, MAE, RMSE; establish prediction intervals and backtesting.
• Translate forecasts into operational decisions and what-if scenarios for planning.
• Collaborate with data engineering to ensure production readiness and reproducibility.

Skills

Forecasting

Python

ML OPS

Pandas

Capacity Planning

Tools

Prophet

PyTorch

TensorFlow

scikit-learn

statsmodels

Job description

Role & responsibilities

Job Description

High-Level Job Description

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

• Proven delivery ofcapacity/demand/time-series forecasting solutions(e.g., workforce/headcount and capacity planning, volume forecasting, infrastructure utilisation, contact-centre demand).
• Strong hands-on capability todesign, 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 asARIMA/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 ofdata leakage, and handlingtrend/seasonality,missing data, andoutliers.

3) Forecast evaluation and operational readiness

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

4) Technical stack and engineering maturity

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

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