Google Cloud AI/ML Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Google Cloud AI/ML Engineer based in India.

In this role, you will design, develop, and deploy production-ready artificial intelligence and machine learning solutions on Google Cloud. You will work on real-world client projects, transforming AI prototypes into reliable, scalable applications that deliver measurable business value. Combining strong machine learning fundamentals with practical software engineering expertise, you will select appropriate approaches, build robust systems, and evaluate performance using data-driven methods. You will also ensure that AI solutions meet strict security, privacy, and cloud infrastructure requirements. Working alongside Google Cloud teams and engaging directly with clients, you will communicate technical trade-offs and take ownership of solutions from initial design through production deployment. This remote opportunity is ideal for an engineer who enjoys solving complex problems, building impactful AI products, and seeing their work deliver results in real-world environments.

Accountabilities

• Design, develop, and deploy end-to-end AI and machine learning solutions on Google Cloud, tailored to client requirements and business objectives.
• Translate business challenges into practical AI/ML applications, selecting appropriate models, algorithms, and architectures based on the problem and available data.
• Build and maintain production-grade AI systems, taking solutions from initial prototypes through testing, deployment, monitoring, and ongoing improvement.
• Develop clean, maintainable, and well-tested Python code that supports reliable, scalable, and efficient AI applications.
• Establish and implement robust evaluation frameworks to measure model quality, assess performance, and guide improvements using quantitative metrics.
• Analyze model outputs and system behavior, using evaluation results to validate changes and ensure solutions meet defined quality and reliability standards.
• Implement secure cloud architectures that protect data and models, applying appropriate networking, access controls, and security practices to meet client requirements.
• Ensure AI applications operate within established data governance, privacy, and infrastructure boundaries.
• Collaborate with clients, Google Cloud teams, and other technical stakeholders to understand requirements, communicate architectural decisions, and explain AI-related trade-offs.
• Troubleshoot production issues, optimize system performance, and continuously improve the reliability and effectiveness of deployed solutions.
• Take end-to-end ownership of technical outcomes, ensuring that delivered AI systems are practical, secure, measurable, and ready for real-world use.

Requirements

• Between 3 and 10 years of experience in software engineering or machine learning engineering, with demonstrated experience delivering production AI solutions on Google Cloud. Strong technical depth and practical results are valued over years of experience alone.
• Solid understanding of machine learning fundamentals, including the ability to evaluate different approaches and select suitable methods for specific problems.
• Hands-on experience designing, building, deploying, and maintaining AI/ML applications in production environments.
• Strong Python programming skills, including writing clean, modular, maintainable, and thoroughly tested code.
• Experience evaluating machine learning models and AI systems using meaningful metrics, structured testing, and data-driven validation techniques.
• Practical knowledge of Google Cloud services and infrastructure used to build, deploy, and operate AI/ML solutions.
• Understanding of cloud networking, security principles, data protection, and access management, with the ability to ensure that models and sensitive data remain within defined security boundaries.
• Ability to move beyond proof-of-concept implementations and deliver robust, scalable, and production-ready AI applications.
• Strong analytical and problem-solving skills, with the ability to investigate technical challenges and make sound engineering decisions.
• Excellent communication skills, including the ability to explain technical concepts, model limitations, and architectural trade-offs clearly to clients and non-specialist stakeholders.
• A proactive, ownership-driven mindset, wit…