ML Ops / Dev Ops Engineer

Imagine.io’s mission is to make 3D simple for everyone. We believe that simplicity in 3D visualization is critical for creating engaging visual content at scale. To go beyond the traditional 2D-3D canvas and create contextual, photo-realistic, and interactive experiences with ease and simplicity will come from vision and creativity. We are working to scale our 3D technology with generative AI models and easy-to-adopt UX, so brands, retailers, and individuals are empowered to generate visual content using 3D.

Imagine.io is financially backed by top VC firms.

Our Product Video – https://www.youtube.com/watch?v=dI3M-Ayrk9g

To learn more, log onto our website- https://imagine.io/

Job Summary:

Imagine.io is seeking an experienced Senior MLOps / DevOps Engineer to join our dynamic team and play a pivotal role in advancing our AI-driven solutions. As an Experienced Senior MLOps / DevOps Engineer, you will lead the design and implementation of cutting-edge MLOps 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, test, release, update, and support processes for DevOps operations to establishing best practices for model versioning, monitoring, and governance.

Designation: ML Ops / Dev Ops Engineer

Job Location: Delhi (Hybrid)

Job Type: Full-Time

Start Date: ASAP

Responsibilities:

• Lead the design and implementation of MLOps solutions to enhance our platform’s capabilities in deploying and managing machine learning models efficiently.
• Defining and setting development, testing, release, update, and support processes for DevOps operation.
• 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.
• Define and enforce best practices for model versioning, monitoring, and governance to ensure the reliability and reproducibility of Generative AI experiments.
• 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 team.

Requirements

• 5+ years of total experience with at least 3+ years of relevant experience in implementing MLOps solutions.
• Bachelor’s or master’s degree in computer science, Data Science, or a related field.
• Must have experience with AWS and Google Cloud, with hands-on experience deploying machine learning models in production environments.
• Scalable Deployment of diffusion models, LLM Models, and Cost-effective deployment of Generative AI products.
• Extensive experience with containerization technologies like Docker and orchestration tools like Kubernetes for managing Generative AI workloads at scale.
• Proficiency in infrastructure as code tools like AWS CloudFormation or Terraform for automating the provisioning and configuration of cloud resources.
• Solid understanding of machine learning principles, algorithms, and techniques, with hands-on experience in developing and deploying machine learning models.
• 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.

Preferred Qualifications:

• Certifications in cloud platforms, such as AWS Certified Machine Learning Specialty or Azure AI Engineer Associate, would be a plus.
• 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