AI Solutions Architect (GCP) @ NY/Remote
Title: AI Solutions Architect (GCP)
Location: New York, NY (Onsite, may consider remote for strong candidates)
Study Level : Bachelor's Degree
Skill Cluster : Data-GCP Vertex AI.
Primary Skill : LLMs, AI/ML, Vertex AI
Experience : 15+ years
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 solutions leveraging machine learning, LLMs, automation, and modern data platforms while ensuring security, scalability, compliance, and operational resilience.
Key Responsibilities:
• Architecture & Strategy 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 Required:
• Bachelors 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 cloud native architecture, APIs, microservices, and event-driven platforms.
• Strong understanding of AI security, model governance, and data privacy controls.
• Experience with data pipelines, databases, and high scale systems.
Preferred:
• Masters degree or equivalent experience.
• Experience with GCP (Vertex AI, BigQuery) and Kubernetes.
• Familiarity with orchestration frameworks, vector databases, embeddings, and model gateways
Mandatory Skills:
• Design 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 architecture artifacts needed to support enterprise-grade solution decisions
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