About Accrete
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. 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.
About the Role
Accrete is seeking a talented and experienced Machine Learning Operations engineer to join their Search Engine AI Team. As the Machine Learning Ops Engineer, you will be responsible for deploying, managing, and monitoring machine learning models in production environments. This role is critical in building several Machine Learning Models and their deployment and operation in a secure, efficient, and effective manner, enabling Accrete to derive maximum value from their machine learning investments.
Responsibilities
- Design the data pipelines and engineering infrastructure to support machine learning systems at scale.
- Take offline models data scientists build and turn them into a real machine learning production system.
- Develop and deploy scalable tools and services to handle machine learning training and inference.
- Identify and evaluate new technologies to improve the performance, maintainability, and reliability of machine learning systems.
- Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
- Support model development with an emphasis on audibility, versioning, and data security.
- Facilitate the development and deployment of proof-of-concept machine learning systems.
- Automate the deployment and scaling of machine learning models in production.
- Monitor and maintain the performance and accuracy of machine learning models in production.
- Implement continuous integration and delivery pipelines for machine learning models.
- Collaborate with data scientists, software engineers, and other stakeholders to ensure machine learning models' effective deployment and operation.
- Ensure the security, privacy, and compliance of machine learning models and related data.
- Stay up to date with the latest technology and industry trends in MLOps.
Requirements
- Bachelor's or master's degree in computer science, Information Technology, or a related field.
- 8+ years of experience in data engineering or a related field.
- Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or equivalent.
- Experience in MLOps tools like MLFlow, and AWS Sagemaker.
- Strong software engineering skills in complex, multi-language systems.
- Fluency in Python.
- Experience working with cloud computing and database systems.
- Experience building custom integrations between cloud-based systems using APIs.
- Experience developing and maintaining ML systems built with open-source tools.
- Experience developing with containers and Kubernetes in cloud computing environments.
- Ability to translate business needs to technical requirements.
- Strong understanding of software testing, benchmarking, and continuous integration.
- Exposure to machine learning methodology and best practices.
- 7-12 Years experience working with Cloud Platforms (i. e., Microsoft Azure, AWS, GCP), Python, Databricks / Hadoop, ML OPS.
- The ideal candidate is curious, consultative, well-organized, articulate, and excited about working in a fast-paced environment.