Platform Engineer, Machine Learning & Data

Accrete is looking to hire a platform engineer to join our growing team. As a Platform Engineer with 2 to 3 years of relevant experience, you will play a crucial role in designing, building, and maintaining our data and machine learning platforms. You will work closely with data scientists, machine learning engineers, data engineers, and other cross-functional teams to ensure the smooth and efficient operation of the infrastructure that drives our business. The ideal candidate should have a solid understanding of machine learning concepts, along with practical experience in developing and managing platforms.

Accrete is an AI prime defense contractor with the U.S. government that creates AI software, enabling its customers to make better decisions, faster. Accrete is on a mission to create AI so powerful it amplifies human reasoning and enables enterprises to grow in previously unimaginable ways. Prior to launching Accrete in 2017, Prashant Bhuyan, Accrete’s Founder and CEO, spent over a decade in high-frequency trading where he and a core team experimented with and developed AI technology that ultimately became the early underpinnings of Accrete.

Accrete’s solutions enable the Department of Defense to predict covert behavior from foreign adversaries seeking to influence the supply chain; the U.S. Air Force to identify vulnerabilities in microprocessor firmware; major music labels to identify superstars before competitors; auto dealers to automatically generate marketing content from vehicle feature lists; employee benefits brokers to identify the shortest path to the hottest leads; and more.

To learn more about Accrete, please visit our website: Accrete.ai

Responsibilities:

• Collaborate with data scientists and machine learning engineers to understand their requirements and design scalable, reliable, and efficient machine learning platform solutions.
• Build and maintain the infrastructure needed to support end-to-end machine learning workflows, including data ingestion, storage, preprocessing, model training, and deployment.
• Develop and optimize data pipelines to ensure the timely and accurate delivery of data to machine learning models.
• Develop systems for the definition and deployment and operation of the different phases of the machine learning and data life cycles.
• Work with Kubernetes to orchestrate and manage containers, ensuring high availability and fault tolerance of applications.
• Monitor and troubleshoot the platform performance, proactively identifying and addressing bottlenecks and issues.
• Stay up-to-date with the latest advancements in technologies and tools, and evaluate their potential to improve our platform's capabilities.
• Document best practices, guidelines, and standard operating procedures for the platform and contribute to knowledge sharing within the team.