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Engineer AI/ML
Career Insights for Machine Learning Engineer
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Scorecard
Based on California data
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$168,439 / year median in California
Job Description
- Must have strong hands-on experience in Python programming, API development, and cloud-native application development (4 Years Minimum).
- Must have experience building prototypes, solution accelerators, and enterprise integrations using cloud technologies.
- Must have two years' experience with Google Cloud Platform (GCP), Vertex AI, Gemini APIs, or similar AI platforms.
- Must have experience in Prompt Engineering, LLM application development, and AI solution implementation.
- Must have experience developing Agentic AI workflows, AI agents, and orchestration frameworks.
- Must have experience with REST APIs, microservices, and third-party system integrations.
- Must have experience working with containerization, CI/CD, and production deployment practices.
- Must possess strong stakeholder communication and customer-facing solutioning skills Roles & Responsibilities
- Design and develop AI-powered prototypes, accelerators, and customer-facing solutions.
- Build and integrate applications using Gemini APIs, Vertex AI, and Google Cloud services.
- Develop agentic workflows, AI assistants, and multimodal solution demonstrations.
- Collaborate with customers, partners, and cross-functional teams to validate business use cases.
- Translate business requirements into scalable technical architectures and solutions.
- Build Proof of Concepts (PoCs) and demonstrate business value through rapid prototyping.
- Support deployment, optimization, and troubleshooting of AI solutions in cloud environments.
- Stay current with emerging AI technologies and apply them to customer scenarios