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Strong employer brand and hybrid setup attracts applicants, but senior machine-learning specialization moderates competition.
Requires deep LLM, Databricks, and production ML experience, so domain-specific backgrounds are strongly preferred.
Multiple mandatory production ML, Databricks, Azure, and LLM requirements create strict technical filtering.
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Own the design and end-to-end delivery of intelligent document processing pipelines using OCR and LLM technologies, including parsing, entity extraction, confidence scoring, and feedback loops.
Develop copilot-style conversational AI assistants over enterprise data with secure authentication and role-based access.
Build and deploy AI/ML pipelines on Databricks and Azure platforms, implementing CI/CD and MLOps practices for production-grade model deployment and monitoring.
Strong Python and PySpark skills with experience shipping ML or data products to production.
Solid foundation in classical machine learning techniques (classification, clustering, similarity, anomaly detection).
Hands-on production experience with large language models including RAG, extraction, evaluation, and prompt design.
Experience deploying on Databricks (Delta Lake, MLflow, performance tuning) and working with Azure AI/data services, identity, networking, and secrets management.
Experienced in building and maintaining AI systems that process documents and enterprise data at scale.
Able to work cross-functionally with business stakeholders to debug issues and explain technical trade-offs clearly.
Disciplined engineer familiar with Git, code reviews, testing, documentation, and using AI coding assistants (e.g., Copilot) daily.