Description

You will build and own Graph-RAG Gen AI solutions for natural language understanding and time series models for financial forecasting. You will engineer high-throughput, scalable systems within the Vertex AI MLOps ecosystem, managing full lifecycle product development and engineering leadership.

Responsibilities

• Deploy out-of-the-box LLMs and Generative AI solutions, including Vector DB setup and Graph RAG implementation.
• Build and maintain automated data and machine learning pipelines for model retraining and continuous monitoring.
• Develop and deploy diverse Machine Learning solutions (GLM, GBM, Neural Networks) and NLP models at scale.
• Handle distributed multi-tiered systems with automated unit and performance testing.
• Manage real-time serving and monitoring of time series data with alerts, ensuring robust observability.

Required Skills

• Python with Machine Learning libraries: TensorFlow, Keras, PyTorch, Scikit-Learn, H2O.ai, XGBoost.
• Experience with Git, CI/CD tools, and distributed version control (GitHub, GitLab, Bitbucket, SVN).
• Proficiency in Elasticsearch, Pyspark, and GCP infrastructure.
• Hands-on experience with Knowledge Graphs and Graph RAG architectures.
• Strong understanding of enterprise infrastructure: Compute, Storage, Network, and Mainframe.
• Experience with solution categories: Orchestration, Container, Model Serving, Observability, and Feature Store.
• 3+ years of full lifecycle product development in highly available, scalable systems.
• Bachelor's degree in Data Science, Mathematics, Computer Science, Engineering, Information Systems, or related STEM field.

Preferred Skills

• Experience with NLP on contracts or time series models with cloud spend data.
• Strong project management and stakeholder management skills.

Key Skills

Education

Bachelor's degree