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Machine Learning Engineer

職缺編號
504996
發布時間
13-五月-2026
工作領域
Research & Development
公司
Siemens Healthcare Private Limited
經驗等級
高級專業人士
工作职位
全職
工作模式
僅辦公室/ 工地
工作性質
長期
地點
  • 班加羅爾 - 卡納塔克邦 - 印度
You will be a key driver in our transition from reactive repairs to a proactive service model. 
By leveraging high-frequency instrument telemetry and service data, you will build the predictive analytics directly impacting how we support our global customer base.

Key Responsibilities

 Proactive Solution Development: Design and deploy ML models to predict component failures and estimate Remaining Useful Life (RUL).
Anomaly Detection: Build robust algorithms to detect silent deviations in machine performance from high-frequency sensor and log data.
Data Product Integration: Collaborate with the team to consume and refine data products from Snowflake and Databricks.
End-to-End ML Pipelines: Develop, test, and scale ML pipelines on Databricks/Snowflake.
Scalable MLOps: Own the end-to-end lifecycle of the models—from experimentation in notebooks to production deployment and monitoring using MLflow.
Actionable Insights: Work with domain experts to ensure model outputs are not just "scores," but clear, actionable steps for field engineers.
GenAI Collaboration: Support the integration of predictive insights into our GenAI-solutions, helping provide context-aware troubleshooting steps based on model outputs.

Technical Skills
The Essentials: Mastery of Python and SQL. Proficiency in PyTorch/TensorFlow.
ML Foundations: Proven experience with classical ML (XGBoost, Random Forest, Scikit-learn) applied to Time-Series or Sensor data.
Predictive Modeling: Strong understanding of Time-Series Analysis, Survival Analysis, and Anomaly Detection (e.g., Isolation Forests, Autoencoders)
Big Data Ecosystem: Hands-on experience with Databricks/Spark for processing large-scale machine datasets.
Statistical Depth: Familiarity with Survival Analysis or reliability engineering concepts is a strong plus.

Nice to Have
Experience with Deep Learning architectures (e.g., LSTMs or GRUs) for sequential data.
Familiarity with GenAI/LLM integration (building tools or agents).
Knowledge of dbt for data modeling within the ML pipeline.