Agiloft

Senior Machine Learning Operations Engineer

Job Description

Position Overview

We are looking for a skilled Senior MLOps Engineer to join our growing efforts in machine learning and generative AI. In this role, you’ll collaborate with Data Scientists, Legal Knowledge Experts, Developers, and other MLEs to design, build, and maintain robust production machine learning infrastructure and tooling. You will play a key role in enabling scalable, efficient, and reliable delivery of customer facing machine learning solutions.


Job Responsibilities
  • Collaborate with cross-functional teams to build and maintain end-to-end machine learning pipelines, from data ingestion to model deployment and monitoring
  • Design, implement, and optimize infrastructure for rapid prototyping, continuous integration, deployment, and model evaluation.
  • Monitor and maintain production machine learning systems to ensure reliability, scalability, and performance
  • Provide technical guidance and mentorship to junior team members and foster knowledge sharing within the MLOps and Data Science teams
  • Stay updated with the latest advancements in MLOps, cloud technologies, and generative AI to identify and implement best practices
  • Set and enforce standards for code quality and best practices across the data science and engineering organizations to ensure maintainability, scalability, and robustness of systems.
  • Other duties as assigned

    • A little bit about you...
  • Proven experience deploying and maintaining machine learning models in production environments
  • Demonstrated ability to gather requirements, design systems, and scope and plan projects effectively, with a focus on the entire machine learning lifecycle
  • Experience with REST API design and implementation, preferably using frameworks such as Flask or FastAPI
  • Proficiency in Python, including both general-purpose programming and machine learning frameworks such as scikit-learn, TensorFlow, PyTorch, or similar
  • Experience in building and scaling machine learning pipelines and infrastructure, including data gathering, feature engineering, model training, and deployment workflows
  • Proficiency with cloud platforms, preferably AWS, and tools like SageMaker, Lambda, or similar
  • Strong communication skills to effectively collaborate with diverse teams, including product managers, engineers, and data scientists
  • Familiarity with CI/CD tools and practices as they apply to machine learning workflows
  • Experience with modern containerization tools such as Docker and Kubernetes

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