Google Cloud AI/ML Engineer
New
J
JobgetherAI/ML cloud
Work remotely from India, Approximately six hours of overlap with North American Eastern TimeFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Between 3 and 10 years of experience in software engineering or machine learning engineering
- Required Skills
- PythonGCPMachine Learning
Requirements
- Have 3–10 years of experience in software engineering or machine learning engineering.
- Have demonstrated experience delivering production AI solutions on Google Cloud.
- Understand machine learning fundamentals and how to evaluate and select approaches for specific problems.
- Have hands-on experience designing, building, deploying, and maintaining production AI/ML applications.
- Have strong Python programming skills and write clean, modular, maintainable, thoroughly tested code.
- Have experience evaluating machine learning models and AI systems using meaningful metrics, structured testing, and data-driven validation.
- Have practical knowledge of Google Cloud services and infrastructure for AI/ML solutions.
- Understand cloud networking, security principles, data protection, and access management.
- Be able to deliver robust, scalable, production-ready AI applications beyond proof-of-concept implementations.
- Be able to investigate technical challenges and make sound engineering decisions.
- Be able to explain technical concepts, model limitations, and architectural trade-offs to clients and non-specialist stakeholders.
- Be able to work independently in a remote environment and collaborate with distributed teams.
- Be available for approximately six hours of overlap with North American Eastern Time.
- Google Cloud certification in machine learning or artificial intelligence is a plus.
Responsibilities
- Design, develop, and deploy end-to-end AI and machine learning solutions on Google Cloud.
- Translate business challenges into AI/ML applications by selecting suitable models, algorithms, and architectures.
- Build and maintain production-grade AI systems from prototypes through testing, deployment, monitoring, and improvement.
- Develop clean, maintainable, well-tested Python code for reliable and scalable AI applications.
- Establish evaluation frameworks to measure model quality and performance using quantitative metrics.
- Analyze model outputs and system behavior to validate changes and meet quality and reliability standards.
- Implement secure cloud architectures using appropriate networking, access controls, and security practices.
- Collaborate with clients, Google Cloud teams, and technical stakeholders on requirements and architectural trade-offs.
- Troubleshoot production issues and optimize system performance, reliability, and effectiveness.
- Take end-to-end ownership of AI solutions from initial design through production deployment.
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