Experience
4 - 6 yrs
Job Location
Pune, India
Vacancy
1
Designation
Lead Machine Learning Engineer
Job Type
Not specified
Job Description
Title: Lead Machine learning
Location: Pune
Exp: 4-6 Years
Key Responsibilities
Design and train the Gate Readiness Prediction model: classify/predict likelihood of gate success across all 6 gates (Pre-KO SOVP) using historical SNPD gate cycle times, deliverable completion rates, and predecessor adherence.
Build the Predictive Risk Heat-map: compound urgency risk scoring across project, part, and supplier dimensions using structured SNPD, SAP cost, and PLM data.
Develop the Supplier Risk Scoring model (Phase 2): supplier profiling using PPAP outcomes, AQP scores, vendor quality history from SAP, and onboarding benchmarks.
Build the Lessons-Learned Recommender (Phase 2): retrieval + ranking model over historical project data, gate outcomes, and documented lessons in the SNPD knowledge base.
Engineer training feature pipelines from the three-tier data architecture: Azure SQL (live), ADLS Gen2 Delta Lake (historical/ML features), and Azure AI Search (vector).
Implement SHAP-based explainability outputs for all predictive models mandatory for gate reviewer trust and IATF 16949 audit traceability.
Set up and maintain MLflow experiment tracking, model registry, and deployment versioning on Azure ML.
Integrate model inference endpoints into SNPD AI microservices; ensure low-latency serving (P95
Implement model monitoring: drift detection, performance degradation alerts, and automated retraining triggers via Azure Data Factory pipelines.
Conduct rigorous model evaluation: precision/recall/F1 for classification tasks, MAE/RMSE for regression; fairness checks across project types (M2/M4/M6) and portfolio buckets.
Collaborate with the Data Engineer on feature store design, training data quality, and CDC-based incremental refresh from SAP/PLM sources.
Document model cards: training data lineage, performance benchmarks, known limitations, and re-training cadence for each deployed model.
TECHNICAL SKILLS REQUIRED Python 3.10+ scikit-learn, XGBoost, LightGBM Azure Data Factory (pipeline orchestration for ML) Time-series forecasting (project timeline prediction) ADLS Gen2 / Delta Lake (feature store reads) Azure Machine Learning (training, registry, endpoints) SQL Server / T-SQL (SNPD unified data model) MLflow experiment tracking model versioning Azure AI Search (embedding-based retrieval) SHAP / LIME model explainability Docker / Azure AKS for model serving Feature engineering from SQL + Delta Lake pandas, NumPy, Plotly (analysis visualization)
GOOD TO HAVE
Experience with automotive or manufacturing NPD data (DFMEA, DVP, PPAP, BOM structures).
Familiarity with SAP S/4 HANA data models (Material Master, BOM, Vendor, Cost Center).
Knowledge of Teamcenter PLM part lifecycle and ECN data structures.
Deep learning with PyTorch / TensorFlow for NLP or document understanding tasks.
Databricks / Apache Spark for large-scale feature computation.
IATF 16949 quality management system awareness.
",Location: Pune
Exp: 4-6 Years
Key Responsibilities
Design and train the Gate Readiness Prediction model: classify/predict likelihood of gate success across all 6 gates (Pre-KO SOVP) using historical SNPD gate cycle times, deliverable completion rates, and predecessor adherence.
Build the Predictive Risk Heat-map: compound urgency risk scoring across project, part, and supplier dimensions using structured SNPD, SAP cost, and PLM data.
Develop the Supplier Risk Scoring model (Phase 2): supplier profiling using PPAP outcomes, AQP scores, vendor quality history from SAP, and onboarding benchmarks.
Build the Lessons-Learned Recommender (Phase 2): retrieval + ranking model over historical project data, gate outcomes, and documented lessons in the SNPD knowledge base.
Engineer training feature pipelines from the three-tier data architecture: Azure SQL (live), ADLS Gen2 Delta Lake (historical/ML features), and Azure AI Search (vector).
Implement SHAP-based explainability outputs for all predictive models mandatory for gate reviewer trust and IATF 16949 audit traceability.
Set up and maintain MLflow experiment tracking, model registry, and deployment versioning on Azure ML.
Integrate model inference endpoints into SNPD AI microservices; ensure low-latency serving (P95
Implement model monitoring: drift detection, performance degradation alerts, and automated retraining triggers via Azure Data Factory pipelines.
Conduct rigorous model evaluation: precision/recall/F1 for classification tasks, MAE/RMSE for regression; fairness checks across project types (M2/M4/M6) and portfolio buckets.
Collaborate with the Data Engineer on feature store design, training data quality, and CDC-based incremental refresh from SAP/PLM sources.
Document model cards: training data lineage, performance benchmarks, known limitations, and re-training cadence for each deployed model.
TECHNICAL SKILLS REQUIRED Python 3.10+ scikit-learn, XGBoost, LightGBM Azure Data Factory (pipeline orchestration for ML) Time-series forecasting (project timeline prediction) ADLS Gen2 / Delta Lake (feature store reads) Azure Machine Learning (training, registry, endpoints) SQL Server / T-SQL (SNPD unified data model) MLflow experiment tracking model versioning Azure AI Search (embedding-based retrieval) SHAP / LIME model explainability Docker / Azure AKS for model serving Feature engineering from SQL + Delta Lake pandas, NumPy, Plotly (analysis visualization)
GOOD TO HAVE
Experience with automotive or manufacturing NPD data (DFMEA, DVP, PPAP, BOM structures).
Familiarity with SAP S/4 HANA data models (Material Master, BOM, Vendor, Cost Center).
Knowledge of Teamcenter PLM part lifecycle and ECN data structures.
Deep learning with PyTorch / TensorFlow for NLP or document understanding tasks.
Databricks / Apache Spark for large-scale feature computation.
IATF 16949 quality management system awareness.
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