Accrete
https://accrete.aiAbout
Accrete AI delivers universally configurable, reliable, and accurate analytical AI agents that perform work that would otherwise require teams of experts and generate insights beyond human capacity.
Our vision is to create a path to ethical, reliable, and trustworthy artificial general intelligence by deploying analytical AI agents across every domain that capture and scale tacit domain knowledge and create net new knowledge reasoned from ground truth. Our mission is to enable our customers to grow in previously unimaginable ways.
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We’re on a mission to build the best-in-class AI Agents that do complex analytical work, compound knowledge, and generate predictive insight beyond human capacity.
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Python
Docker
Machine Learning (ML)
KubernetesResponsibilities:
- Collaborating with data scientists and machine learning engineers to understand their requirements and design scalable, reliable, and efficient machine learning platform solutions.
- Building and maintaining the applications and infrastructure to support end-to-end machine learning workflows, including inference and continual training.
- Developing systems for the definition deployment and operation of the different phases of the machine learning and data life cycles.
- Working within Kubernetes to orchestrate and manage containers, ensuring high availability and fault tolerance of applications.
- Documenting the platform's best practices, guidelines, and standard operating procedures and contributing to knowledge sharing within the team.
Requirements:
- 3+ years of hands-on experience in developing and managing machine learning or data platforms
- Proficiency in programming languages commonly used in machine learning and data applications such as Python, Rust, Bash, Go
- Experience with containerization technologies, such as Docker, and container orchestration platforms like Kubernetes.
- Familiarity with CI/CD pipelines for automated model training and deployment. Basic understanding of DevOps principles and practices.
- Knowledge of data storage solutions and database technologies commonly used in machine learning and data workflows.
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Engineering Centric Culture
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Meaningful and ever-evolving work
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A balanced workspace
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A level hierarchy
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Hybrid work mode
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