Role overview
- 10+ yrs experience minimum
- Collaborate and manage with data science , engineering , and GenAI teams to deploy and scale machine learning and generative AI models.
- Operationalize complex ML and GenAI models into production environments, ensuring end-to-end deployment and monitoring.
- Apply knowledge of standard ML algorithms (Regression, Classification), NLP concepts (sentiment analysis, topic modeling, TF-IDF), and Generative AI techniques (LLMs, prompt engineering, embeddings).
- Apply knowledge of Retrieval Augmented Generation using embedding models and Vector databases.
- Manage delivery of GenAI/LLM features (prompt engineering, evaluation metrics, retrieval patterns, guardrails) and productionizing Q&A/assistant workflows.
- Lead Platform and DevOps: CI/CD, containerization, observability, and environment automation in a major cloud - ideally working experience on Google .
- Utilize Python and ML/GenAI libraries such as scikit-learn , PySpark , pandas and Hugging Face Transformers for model development and optimization.
- Design, develop, and maintain adaptable data pipelines tailored to use-case-specific requirements.
- Integrate ML and GenAI use cases into business workflows , ensuring seamless data exchange with upstream and downstream systems.
- Build and maintain pipelines for model performance metrics , supporting Model Risk Oversight and compliance review cadences.
- Develop runbooks and provide ongoing support for operationalized models to ensure reliability and scalability.
Tags & focus areas
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