What are the responsibilities and job description for the Senior AISolution Architect position at Comprehensive Resources Inc.?
Job Title: Senior AI Solution Architect - Data & AI Platforms (Google Cloud Platform)
Location: Santa Ana, CA
Duration : 12 months contract
Experience
10-15 years overall experience (with 5 years in AI/Data Architecture roles)
Job Summary
We are seeking a highly skilled AI Solution Architect with deep expertise in Data Architecture, AI/ML platforms, and Generative AI solutions, to design and deliver scalable, secure, and enterprise-grade data and AI solutions on Google Cloud Platform (Google Cloud Platform).
The ideal candidate will have strong hands-on experience across data lakehouse architectures, modern BI platforms, ML/MLOps, Conversational Analytics, Generative AI, and Agentic AI frameworks, and will work closely with business, data engineering, and AI teams to drive end-to-end AI-led transformation.
Key Responsibilities
Data & Platform Architecture
Design and own end-to-end data architectures including ingestion, processing, storage, governance, and consumption layers
Architect modern data lakehouse platforms using Google Cloud Platform services (e.g., BigQuery, Dataproc, Cloud Storage)
Define scalable data platforms supporting batch, streaming, and real-time analytics
Establish data governance, metadata management, data quality, lineage, and security frameworks
AI, ML & MLOps Architecture
Design ML/AI architectures supporting model training, deployment, monitoring, and lifecycle management
Define and implement MLOps frameworks (CI/CD for ML, feature stores, model registries, observability)
Collaborate with data scientists to productionize ML models at scale
Evaluate and recommend ML frameworks, tools, and best practices
Generative AI & Agentic AI
Architect and implement Generative AI solutions using LLMs (e.g., text, code, embeddings, multimodal use cases)
Design Conversational Analytics and AI-powered BI solutions
Build and evaluate Agentic AI platforms, including autonomous agents, orchestration frameworks, and tool integrations
Lead solution evaluations, PoCs, and vendor/tool assessments for GenAI and Agent-based systems
Business Intelligence & Analytics
Design modern BI and analytics platforms enabling self-service analytics and AI-driven insights
Integrate BI tools with data lakehouse and AI layers
Enable semantic layers, metrics definitions, and governed analytics
Cloud & Google Cloud Platform Leadership
Lead architecture and solution design on Google Cloud Platform (Google Cloud Platform)
Utilize Google Cloud Platform services such as BigQuery, Vertex AI, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Looker, and IAM
Ensure architectures follow best practices for security, scalability, performance, and cost optimization
Stakeholder & Technical Leadership
Partner with business leaders to translate business requirements into AI-driven solutions
Lead technical design reviews and architecture governance
Mentor engineers, architects, and data scientists
Create architecture blueprints, reference architectures, and technical documentation
Required Skills & Qualifications
Core Technical Skills
Strong experience in Data Architecture & Data Platforms
Hands-on expertise in Data Lakehouse architectures
Deep understanding of end-to-end data management
Experience with modern BI platforms and analytics ecosystems
Strong background in AI/ML architecture and MLOps
Proven experience in Conversational Analytics and Generative AI
Hands-on exposure to Agentic AI platforms, frameworks, and evaluations
Strong expertise in Google Cloud Platform (Google Cloud Platform)
Tools & Technologies (preferred)
Google Cloud Platform: BigQuery, Vertex AI, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Looker
AI/ML: TensorFlow, PyTorch, scikit-learn, LLM frameworks
MLOps: CI/CD, feature stores, model registries, monitoring tools
Data: SQL, Python, Spark, Kafka
BI: Looker, Tableau, Power BI (or equivalent)
Preferred Qualifications
Bachelor s or Master s degree in Computer Science, Data Science, Engineering, or related field
Google Cloud Platform Professional certifications (e.g., Professional Data Engineer, Professional ML Engineer, Cloud Architect)
Experience working in large-scale enterprise or consulting environments
Strong communication and stakeholder management skills