Machine Learning Ops-Software Engineer
2 weeks ago
Responsibilities:
Develop and maintain machine learning pipelines to support our machine learning models.
Ensure the integration and maintenance of model and prompt libraries.
Assist in fine-tuning, testing, and deploying sophisticated machine learning models.
Utilize Infrastructure as Code (IaC) for managing and provisioning through the complete lifecycle of cloud resources.
Collaborate closely with the Data Engineering and our Artificial Intelligence and Machine Learning teams to ensure seamless adoption of traditional ML and Large Language Models into our products
Develop, integrate, automate, and deploy to optimize the interaction between different system components.
Minimum Requirements:
Bachelor's degree in computer science, engineering, mathematics, or a related field
3+ years software experience object-orientedlanguage (Python)
Experience with Data Pipelines related to ML workflows
Infrastructure-as-Code deployments (terraform)
Experience working with Traditional ML and tools
Experience with Large Language Models(such as OpenAI GPT Models, Llama2)
Experience optimizing Large Language Models (LoRA, Quantization)
Experience in Natural Language Processing (Word Embeddings, Text Summarization, Intent Detection, Named Entity Recognition)
Experience within Financial Services Industry or products a bonus
Some of the areas you will be working on:Working with traditional Machine Learning Techniques and tools
Workingon deploying MLOps and LLMOps Tools and Ecosystems such as MLFlow, AWS Sagemaker, GCP VertexAI or comparable ML tooling across the firm
Managing, optimizing data pipelines related to RAG and other ML Workflows
Usage of Python in a data-intensive environment
Working to deploy, automate withcloud-based IaC tools for fully automated deployments
Using and leveraging REST interfaces and various API endpoints to integrate multiple tools at FactSet
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