AI Solution Architect

  • Posted 13 hours ago | Updated 13 hours ago

Overview

Remote
Depends on Experience
Contract - W2
Contract - Independent
Contract - 12 Month(s)

Skills

Artificial Intelligence
Machine Learning (ML)
Machine Learning Operations (ML Ops)
GCP
Vertex AI
LLM
AI/ML

Job Details

Job Title : AI Solution Architect

Location: NYC, NY

Skill Cluster : Data-Google Cloud Platform Vertex AI.

Primary Skill : LLMs, AI/ML, Vertex AI

Job Description :

The AI Solutions Architect defines the technical direction for AI, ML, and data-driven capabilities across the enterprise. The role shapes the architecture for intelligent, cloud-native solutionsleveraging machine learning, LLMs, automation, and modern data platformswhile ensuring security, scalability, compliance, and operational resilience.Key Responsibilities Architecture & Strateg spanning data pipelines, training workflows, inference layers, and governance. Translate business priorities into AI-enabled solution designs and reusable reference architectures. , MLOps workflows, and cost efficiency across AI workloads. Maintain and evolve architecture standards; champion adoption across delivery teams.Leadership & Influence . Mentor engineering teams and contribute to architecture communities of practice.Qualifications RequiredBachelors degree in Computer Science or related field. 15+ years in software architecture with strong experience in distributed systems. 5+ years designing or implementing AI/ML or LLM-based solutions. 5+ years hands-on development experience (Java/Kotlin/C#/Python). Expertise in cloudnative architecture, APIs, microservices, and event-driven platforms. Strong understanding of AI security, model governance, and data privacy controls. Experience with data pipelines, databases, and highscale systems.Preferred Masters degree or equivalent experience. Experience with Google Cloud Platform (Vertex AI, BigQuery) and Kubernetes. Familiarity with orchestration frameworks, vector databases, embeddings, and model gatewaysMandatory SkillsDesign end-to-end architectures for AI/ML and LLM-based solutions, spanning data pipelines, training workflows, inference layers, and governance. Translate business priorities into AI-enabled solution designs and reusable reference architectures. Evaluate emerging AI and cloud capabilities and guide technology adoption aligned with enterprise strategy. Engage early with the Governance organization to align on standards, secure required approvals, and deliver the architecturaartifacts needed to support enterprise-grade solution decisions

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