Aligned Automation
From Aligned Automation's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
You will work in agile teams, contributing to high-quality code and ensuring smooth operations across the ML infrastructure.
About the Job About Aligned Automation At Aligned Automation, we live by our "Better Together" philosophy to build a better world. As a strategic service provider to Fortune 500 companies, we help digitize enterprise operations and drive impactful business strategies. Our purpose goes beyond projects—we strive to deliver meaningful, sustainable change that shapes a more optimistic and equitable future. Our culture is deeply rooted in our 4Cs—Care, Courage, Curiosity, and Collaboration—ensuring that each employee is empowered to grow, innovate, and thrive in an inclusive workplace. Job Description: We are looking for a seasoned ML Engineer (MLOps) to join our team and drive the development and deployment of scalable machine learning solutions. The ideal candidate will have deep expertise in building robust ML pipelines, integrating large language models (LLMs), and managing model lifecycle using tools like MLFlow. You will work in agile teams, contributing to high-quality code and ensuring smooth operations across the ML infrastructure. Key Responsibilities: Participate in Scrum ceremonies and contribute to sprint planning and retrospectives. Scope and resolve technical issues related to ML pipelines and infrastructure. Write and implement clean, scalable, and maintainable code for ML workflows. Submit and manage pull requests, ensuring code quality through liners and scanners. Conduct and participate in code reviews to maintain high standards. Collaborate with data scientists and engineers to deploy and monitor ML models. Manage model lifecycle using MLFlow Hub and integrate with cloud-native solutions. Work with LLMs to build intelligent applications and services. Technical Skills: Strong proficiency in Python for ML and MLOps tasks. Experience with databases (especially Postgres). Familiarity with object storage systems like Amazon S3. Hands-on experience with LLMs and their integration into production systems. Proficient in using MLFlow Hub for model tracking and deployment. Comfortable with GitHub workflows and version control. Technology Stack: Programming & Scripting: Python Databases: Postgres Cloud & Storage: Amazon ECS, S3 ML Tools: MLFlow Hub, LLMs Version Control & Collaboration: GitHub
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