Google Cloud AI/ML Engineer

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, with the ability to work independently in a remote environment and collaborate effectively with distributed teams.
Availability for approximately six hours of overlap with North American Eastern Time to support collaboration with clients and project teams.
A Google Cloud certification in machine learning or artificial intelligence is a plus.
Experience working directly with enterprise clients or collaborating with Google Cloud partner teams is advantageous.
Benefits
Remote work: Work remotely from India, with flexibility to manage your work in a distributed environment.
Flexible engagement: Opportunity to work on a contract or full-time basis, depending on the agreed arrangement.
Competitive compensation: Compensation aligned with your experience, technical expertise, and engagement terms.
Real-world AI projects: Build and deploy AI solutions that are used by real clients in production environments.
Google Cloud collaboration: Work on projects referred through Google and collaborate with Google Cloud teams.
Technical growth: Strengthen your expertise in cloud-based machine learning, production AI systems, model evaluation, and secure cloud architecture.
End-to-end ownership: Take responsibility for the full AI development lifecycle, from solution design to deployment and continuous improvement.
Meaningful impact: Help organizations turn AI capabilities into secure, reliable products that deliver measurable business outcomes.
Collaborative environment: Engage with technical experts and clients across North America while contributing to challenging AI initiatives.