Description

You will design and architect the end-to-end time series ML lifecycle and generative AI pipelines.

Responsibilities

• Architect time series ML lifecycles including ingestion, feature engineering, training, and serving for univariate and multivariate models.
• Build complete end-to-end RAG pipelines and integrate anomaly segmentation techniques.
• Establish MLOps best practices using MLflow for model tracking, versioning, and CI/CD pipelines.
• Deploy models across batch Databricks jobs and real-time Azure ML or AKS endpoints.
• Design feature pipelines using Delta Lake and offline/online feature stores.
• Lead data scientists and ML engineers to prototype and productionize energy optimization models.

Required Skills

• 5+ years of experience in AI and software engineering.
• Expertise in Azure Databricks, Azure ML, and Azure Kubernetes Service (AKS).
• Experience with Azure Data Lake, Delta Tables, and Azure Blob Storage.
• Proficiency in time series and predictive modeling (ARIMA, Prophet, LSTM, XGBoost).
• Hands-on experience with RAG/LLM based Generative AI.
• Knowledge of CI/CD pipelines and MLOps lifecycle management.
• Experience with Bronze, Silver, and Gold zone data processing.
• Ability to design feature stores using Feast or Delta + Redis.

Key Skills

Education

Any Graduate