Senior Software Engineer (MLOps)

FactSet’s product suite of smart analytics and unique data empowers the world’s leading financial service professionals to make more informed decisions every day. At our heart is an inclusive community unified by the spirit of going above and beyond. Our philosophy is to embrace diversity and that our best ideas can come from anyone, anywhere, anytime. We continuously look ahead to advance the future and technology of our industry by rolling up our sleeves to solve tough problems together and by learning from our successes and failures.

FactSet is seeking an experienced Machine Learning Operations Engineer to lead the development and maintenance of our next-generation Machine Learning Platform. The successful candidate will be responsible for integrating and maintaining model and prompt libraries, assisting our software and machine learning engineers in fine-tuning and deploying models, championing emerging AI technologies, and promoting good data practices. This position involves managing complex ML pipelines, harnessing cloud infrastructure, and utilizing Python and REST interfaces to enable Commercial and Open-Source Large Language Models at FactSet.

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:

• 3+ years of software experience in object-oriented language

Critical Skills:

• Experience with Data Pipelines related to ML workflows.
• Infrastructure-as-Code deployments
• Experience working with Traditional ML and tools.
• Experience with Large Language Models (such as OpenAI GPT Models, Llama2)

Additional Skills:

• Experience within the Financial Services Industry or products a bonus

Some of the areas you will be working on:

• Working with traditional Machine Learning Techniques and tools
• Working on deploying MLOps and LLMOps Tools and Ecosystems such as MLFlow, AWS Sagemaker, GCP Vertex AI or comparable ML tooling across the firm
• Managing and optimizing data pipelines related to RAG and other ML Workflows
• Usage of Python in a data-intensive environment
• Working to deploy and automate with cloud-based IaC tools for fully automated deployments.
• Using and leveraging REST interfaces and various API endpoints to integrate multiple tools at FactSet.

Education:

• Bachelor’s degree in computer science, engineering, mathematics, or a related field