Sr. ML Platform Engineer

5 Star Recruitment; Mumbai; On-site; INR 3,000,000 - 4,500,000; Full time

Job summary

5 Star Recruitment in Mumbai is seeking a seasoned ML Platform Engineer to design and implement scalable MLOps platforms for Fortune 500 clients, enabling seamless development, deployment, and maintenance of machine learning models.

The successful candidate will leverage deep expertise in cloud platforms and DevOps practices to bridge the gap between data science and operations, contributing to transformative AI projects.

Qualifications

• Bachelor's or master's degree in Engineering, Computer Science or equivalent experience.
• 10+ years of professional experience building cloud-based applications, including ML platforms.
• Experience with cloud platforms like AWS, Google Cloud, or Azure (Azure preferred).
• Proficient in DevOps, CI/CD, containerization, and infrastructure as code.
• Strong knowledge of ML workflows, model training and deployment processes.

Responsibilities

• Evaluate and select appropriate cloud services for each stage of the ML lifecycle.
• Design and implement the overall architecture of the MLOps platform.
• Set up automated pipelines for data preparation, model training, and deployment.
• Implement version control for code, data, and models to maintain reproducibility and traceability.
• Ensure the platform is scalable, secure, and compliant with relevant regulations.
• Provide tools and interfaces for data scientists to easily leverage the platform.
• Continuously optimize the platform for performance and cost-efficiency.
• Bridge the gap between data science and operations, enabling efficient development, deployment, and maintenance of ML models at scale.

Skills

Cloud platforms

DevOps practices

ML workflows

Python for ML

CI/CD pipelines

Docker/Kubernetes

Education

Bachelor's/Master's in Engineering/CS

Tools

Docker

Kubernetes

Infrastructure as Code

Job description

Rs 3,000,000.00 - 4,500,000.00 (Indian Rupee)

We areseeking a seasoned ML Platform Engineer. This role involves designing and implementing scalable MLOps platforms for Fortune 500 clients, enabling seamless development, deployment, and maintenance of machine learning models. The successful candidate will leverage deep expertise in cloud platforms and DevOps practices to bridge the gap between data science and operations, contributing to transformative AI projects.

Job Responsibilities:

• Evaluate and select appropriate cloud services for each stage of the ML lifecycle.
• Design and implement the overall architecture of the MLOps platform.
• Set up automated pipelines for data preparation, model training, and deployment.
• Implement version control for code, data, and models to maintain reproducibility and traceability.
• Ensure the platform is scalable, secure, and compliant with relevant regulations.
• Provide tools and interfaces for data scientists to easily leverage the platform.
• Continuously optimize the platform for performance and cost-efficiency.
• Develop tools and interfaces for data scientists to easily utilize the platform's capabilities.
• Bridge the gap between data science and operations, enabling efficient development, deployment, and maintenance of ML models at scale.

Job Requirements:

• Bachelors/Masters degree in Engineering, Computer Science, or equivalent experience.
• At least 10+ years of professional experience in building cloud-based applications, including ML platforms.
• Extensive expertise in cloud platforms like AWS, Google Cloud Platform, or Azure (Azure preferred).
• Proficiency in DevOps practices, including CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure as code.
• Strong understanding of ML workflows, model training, deployment processes, and data engineering pipelines.
• Advanced programming skills, particularly in Python, with a focus on ML applications.
• Experience in designing secure, scalable systems that adhere to regulatory compliance.
• Demonstrated ability to collaborate with cross-functional teams, including data scientists and software engineers.

Must-Haves
Total Experience: 10 years.

Recent Experience: 2-4 years in ML Ops.

Previous Experience: Remaining years in DevOps.

Preferred Background: DevOps professional transitioned into an ML Ops engineer.