Not specified

Job Summary

We are seeking a skilled professional to design, implement, and manage end-to-end systems that support the machine learning lifecycle. The successful candidate will contribute to building and maintaining robust data pipelines, integrating ML models into production environments, and ensuring efficient CI/CD processes. This role offers the opportunity to work on complex, multi-language systems in a dynamic and collaborative environment.

Responsibilities

• Develop and Deploy Systems: Design, implement, and maintain cloud-based machine learning systems, containerized applications, and data pipelines.
• Manage Integrations: Work with ML frameworks (TensorFlow, PyTorch, Keras) and generative AI frameworks to integrate advanced modeling techniques into production.
• Implement CI/CD Processes: Develop and automate CI/CD pipelines using tools such as Jenkins, GitLab CI, or similar, ensuring seamless and reliable deployments.
• Collaborate with Teams: Partner with cross-functional teams to troubleshoot complex production issues, optimize systems, and enhance overall performance.
• Maintain Best Practices: Uphold strong software engineering standards across multi-language systems and ensure adherence to best practices in ML model serving and lifecycle management.

Qualifications

• Educational Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Cloud Experience: Proven experience with cloud platforms like AWS, GCP, and Azure.
• Containerization: Proficiency with containerization tools such as Docker and Kubernetes.
• Programming Skills: Strong expertise in Python and Shell scripting, with familiarity in ML frameworks including TensorFlow, PyTorch, and Keras.
• ML Lifecycle Knowledge: Solid understanding of the machine learning lifecycle, data pipelines, and model serving.
• CI/CD Proficiency: Experience with CI/CD tools such as Jenkins, GitLab CI, or comparable solutions.
• Software Engineering: Strong software engineering skills in building and maintaining complex, multi-language systems.
• Problem Solving & Communication: Excellent troubleshooting abilities alongside strong communication skills for effective cross-functional collaboration.

Preferred Skills

• Experience with generative AI frameworks.
• Exposure to deep learning approaches and advanced modeling techniques.

Experience

• Proven track record in building end-to-end systems, whether as a Platform Engineer, ML DevOps Engineer, Data Engineer, or in a comparable role.
• Relevant experience in software engineering, machine learning operations, and continuous integration/deployment within complex production environments.

Environment

• Work setting details such as location, remote work options, and office environment are not specified. However, the role is expected to function in a dynamic, collaborative, and fast-paced setting that may include flexible work arrangements.