Machine Learning Engineer, Factset

• Your extensive expertise in deploying state-of-the-art solutions including Graph Technologies, NLP, predictive analytics, Large Language Models (LLM), and cloud-native technologies will be crucial
• This role is perfect for someone with a passion for tackling complex problems within the financial domain and has a proven ability to deliver robust, high-performance AI systems
• 3-5 years of profound software engineering experience, significantly focused on AI/ML solutions in production environments
• Demonstrated expertise in cloud architecture (primarily AWS) and familiarity with a broad range of services
• Solid understanding of Natural Language Processing/Machine Learning/Deep Learning fundamentals and their real-world applications, evidenced by a successful history of model development and deployment
• Proficient in Python, with strong skills in Docker and API development
• Excellent communication abilities, capable of engaging both technical and business audiences alike, and leading cross-functional projects
• Knowledge of major database architectures including MongoDB, SQL, NoSQL, and Vector databases
• An MS degree in Machine Learning, Computer Science, or a related field is preferred

Desirable

• Join FactSet's Data Solutions AI team as a AI/ML Engineer to drive forward-thinking innovations in our financial AI applications
• Architect and design groundbreaking machine learning techniques tailored to financial tasks within Knowledge Graphs, creating innovative solutions that extend beyond traditional applications
• Enhance and scale our AWS-based infrastructure to ensure the efficient, reliable delivery of ML and AI solutions, including the integration of LLM
• Work closely with data scientists and ML engineers to integrate and manage diverse ML and NLP models within production environments effectively. Offer expert advice on model selection and deployment strategies
• Manage the entire software development lifecycle, from the initial design and coding through to testing and the deployment of financial AI applications
• Construct and maintain robust data pipelines capable of processing complex structured and unstructured financial data, guaranteeing the highest quality inputs for our models
• Act as a mentor to team members, promoting a culture of innovation and continuous learning within the team