Balin Technologies

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)