Job Title: Senior AI Solution Architect - Data & AI Platforms (Google Cloud Platform)
Location: Santa Ana, CA
Duration: Fulltime
Job Description:
Primary Skills: Big Query, Vertex AI, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Looker,Data Architecture, AI/ML platforms, and Generative AI solutions,datalake house architectures, modern BI platforms, ML/MLOps, Conversational Analytics, Generative AI, and Agentic AI frameworks
Secondary Skills: Looker, Tableau, Power BI, SQL, Python, Spark, Kafka
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 datalake house 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)
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