Machine Learning Engineer

2 months ago

London, Greater London, United Kingdom FactSet Full time

About the Role

As a Machine Learning Engineer - Operations, you will play a pivotal role in building and maintaining the infrastructure that powers our cutting-edge machine learning models. You'll be responsible for developing robust pipelines, ensuring seamless integration of models and libraries, and collaborating closely with data engineering and AI teams to deliver innovative solutions.

Key Responsibilities

  • Design, develop, and maintain machine learning pipelines to support the deployment and scalability of our models.
  • Ensure the smooth integration and management of model and prompt libraries within our ecosystem.
  • Contribute to the fine-tuning, testing, and deployment of sophisticated machine learning models across various platforms.
  • Leverage Infrastructure as Code (IaC) principles to manage and provision cloud resources throughout their lifecycle.
  • Collaborate effectively with Data Engineering and AI teams to facilitate the seamless adoption of traditional ML and Large Language Models into our products and services.
  • Develop, integrate, automate, and deploy solutions to optimize the interaction between different system components, enhancing overall efficiency.

Qualifications

  • 3+ years of software development experience in object-oriented programming languages.

Essential Skills

  • Proven experience with data pipelines specifically tailored for machine learning workflows.
  • Expertise in Infrastructure as Code deployments and cloud infrastructure management.
  • Hands-on experience working with traditional machine learning techniques and tools.
  • Familiarity with Large Language Models (LLMs) such as OpenAI GPT models and Llama2.

Additional Assets

  • Experience within the Financial Services industry or related domains is highly valued.

Areas of Focus

  • Implementation and optimization of traditional machine learning techniques and tools.
  • Deployment and management of MLOps and LLMOps tools and ecosystems, including MLFlow, AWS Sagemaker, GCP Vertex AI, or comparable solutions.
  • Management and optimization of data pipelines for Retrieval Augmented Generation (RAG) and other machine learning workflows.
  • Proficiency in Python within a data-intensive environment.
  • Utilization of cloud-based IaC tools to automate deployments and ensure infrastructure scalability.
  • Integration of various tools through REST interfaces and API endpoints to enhance system interoperability.

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