Braintrust
Job Description We are looking for a highly energetic and collaborative Senior Data Scientist with experience building enterprise level GenAI applications, designed and developed MLOps pipelines .
The ideal candidate should have deep understanding of the NLP field, hands on experience in design and development of NLP models and experience in building LLM-based applications.
Skills: Python, Pyspark, Pytorch, Langchain, GCP, Web development, Docker, Kubeflow
Key Responsibilities:
- Work with AI/ML Platform Enablement team within the eCommerce Analytics team. The broader team is currently on a transformation path, and this role will be instrumental in enabling the broader team’s vision.
- Work closely with other Data Scientists to help with production models and maintain them in production.
- Deploy and configure Kubernetes components for production cluster, including API Gateway, Ingress, Model Serving, Logging, Monitoring, Cron Jobs, etc. Improve the model deployment process for MLE for faster builds and simplified workflows
- Be a technical leader on various projects across platforms and a hands-on contributor of the entire platform’s architecture
- Responsible for leading operational excellence initiatives in the AI/ML space which includes efficient use of resources, identifying optimization opportunities, forecasting capacity, etc.
- Design and implement different flavors of architecture to deliver better system performance and resiliency.
- Develop capability requirements and transition plan for the next generation of AI/ML enablement technology, tools, and processes to enable to efficiently improve performance with scale.
Tools/Skills (hands-on experience is must):
- Ability to transform designs ground up and lead innovation in system design
- Deep understanding of GenAI applications and NLP field
- Hands on experience in the design and development of NLP models
- Experience in building LLM-based applications
- Design and development of MLOps pipelines
- Fundamental understanding on the data science parameterized and non-parameterized algorithms.
- Knowledge on AI/ML application lifecycles and workflows.
- Experience in the design and development of an ML pipeline using containerized components.
- Have worked on at least one Kubernetes cloud offering (EKS/GKE/AKS) or on-prem Kubernetes (native Kubernetes, Gravity, MetalK8s)
- Programming experience in Python, Pyspark, Pytorch, Langchain, Docker, Kubeflow
- Ability to use observability tools (Splunk, Prometheus, and Grafana ) to look at logs and metrics to diagnose issues within the system.
Additional Information:
- Resource can be remote in India but be ready to work during CST time zone.
- This is a hands-on position and will be working with a team of customer data scientists & ML engineers.
- The person should be able to explain his or her work and be able to go through models/code.