Find Jobs
Find Jobs Near You – Available Work in Your Location
Technical Architect AI/ML
Career Insights for Machine Learning Engineer
See where this job fits in the broader career landscape. Knowing your career path helps you see what's possible from here.
Scorecard
Based on California data
Review key factors to help you decide if this role fits your goals. How is this calculated?
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 15+ years of experience in enterprise architecture, solution design, and large-scale digital transformation programs.
- Must have deep expertise in Google Cloud Platform (GCP), Vertex AI, Gemini, AI Agents, and cloud-native architectures.
- Must have extensive experience designing Enterprise AI, GenAI, Agentic AI, RAG, and multi-agent solutions.
- Must have proven experience leading end-to-end architecture for mission-critical, scalable, and secure platforms.
- Must have expertise in distributed systems, microservices, APIs, event-driven architectures, and platform engineering.
- Must have strong experience in AI governance, responsible AI, security, compliance, and enterprise adoption frameworks.
- Must have experience driving executive stakeholder management, technology strategy, and business transformation initiatives.
- Must have experience leading global engineering teams, architects, and customer engagements across multiple industries. Roles & Responsibilities
- Define and drive the enterprise AI and cloud architecture strategy for strategic customer engagements.
- Lead large-scale AI transformation programs leveraging Gemini, Vertex AI, Agentic AI, and Google Cloud services.
- Establish architecture standards, governance frameworks, and AI adoption roadmaps.
- Engage with C-level executives to align technology investments with business outcomes.
- Lead architecture reviews, technical due diligence, and solution assurance activities.
- Design reference architectures, accelerators, reusable frameworks, and industry-specific AI solutions.
- Mentor architects and engineering leaders while driving technical excellence across programs.
- Collaborate with Google product, engineering, partner, and customer teams to shape strategic AI initiatives.