UltraGenius

MLOps Engineer

seeking an experienced Senior DevOps Engineer to join our dynamic team and play a pivotal role in advancing our AI-driven solutions. As an Experienced Senior DevOps Engineer, you will lead the design and implementation of cutting-edge MLOps and DevOps solutions, leveraging your extensive expertise to optimize performance, scalability, and reliability. You'll collaborate closely with multidisciplinary teams to develop end-to-end CI/CD pipelines tailored for machine learning workflows and help in defining and setting development, testing, release, update, and support processes for DevOps and MLOps operations to establishing best practices for model versioning, monitoring, and governance.

 

Designation: MLOps Engineer

Job Location: Remote

Job Type: Full-Time

Start Date: ASAP

 

Responsibilities:

• Lead the design and implementation of MLOps and DevOps solutions to enhance our platform's capabilities.


• Defining and setting development, testing, release, update, and support processes for DevOps and MLOps operations.


• Deployment of diffusion models, LLM Models, and Cost-effective deployment of Generative AI products.


• Collaborate closely with data scientists, machine learning engineers, and product developers to integrate Generative AI models into our existing infrastructure seamlessly.


• Develop and implement end-to-end CI/CD pipelines specifically tailored for Generative AI model workflows to ensure reliable and automated model deployment.


• Optimize and fine-tune infrastructure services, including PaaS and IaaS, to maximize performance, scalability, and cost efficiency for machine learning applications.


• Stay updated with the latest advancements in MLOps tools, techniques, and industry trends to drive innovation and continuously improve our MLOps practices.


• Provide technical leadership and mentorship to junior team members to foster a culture of continuous learning and growth within the MLOps and DevOps team.

 

Requirements

 

• 5+ years of total experience in DevOps with relevant experience in implementing MLOps solutions.


• Must have experience with Google Cloud, with hands-on experience deploying machine learning models in production environments.


• Experienced in the scalable and cost-effective deployment of machine learning models and other modules.


• Extensive experience with containerization technologies like Docker and orchestration tools like Kubernetes for managing workloads at scale.


• Proficiency in Infrastructure as code (IaC) for automating the provisioning and configuration of cloud resources.


• Experience with DevOps practices and tools, including CI/CD pipelines, version control systems, and automated testing frameworks.


• Excellent problem-solving skills and the ability to troubleshoot and debug complex MLOps workflows.

 

Good to have:

• Experienced in deployment of diffusion models, LLM Models, and Generative AI products


• Certifications in cloud platforms, such as AWS and Google Cloud.


• Experience with MLOps tools and frameworks, such as Kubeflow, MLflow, or TFX, for managing the end-to-end machine learning lifecycle.


• Knowledge of data engineering principles and techniques for building scalable and reliable data pipelines.


• Familiarity with software development methodologies, such as Agile or Scrum, for iterative and collaborative project management.

Benefits

• Build products from scratch and be part of decision making.


• Freedom to explore and implement your own ideas


• Hybrid Work Mode


• Open culture with flexible timings


• Work with a Team who is a Family

devops machine-learning