What are the responsibilities and job description for the GCP Gen AI Architect position at Programmers.io?
Job Description:
Position: GCP Gen AI Architect
Location: Irving, TX / Jacksonville, FL (Onsite)
Hire Type: Full Time / Contract
Experience: 15 Years
Domain: Banking, Fraud, Risk & Controls
We are seeking a highly experienced Generative AI Architect to lead the architecture, design, and implementation of next-generation AI solutions. The ideal candidate should possess deep expertise in Generative AI, Agentic AI, GCP Cloud Architecture, Customer Data Platforms (CDP), and enterprise-scale banking solutions, with a proven track record of delivering AI-driven transformation programs.
The role requires close collaboration with business stakeholders, product teams, data architects, risk teams, and engineering leadership to build scalable, secure, and compliant AI solutions leveraging Google Cloud's AI ecosystem.
Key Responsibilities:
Define and drive the overall AI and data architecture strategy for the OMAI program.
Architect enterprise-scale GenAI solutions leveraging LLMs, Agentic AI, RAG, Knowledge Graphs, and AI Agents.
Design and implement Customer Data Platform (CDP) integrations to enable unified customer intelligence and personalized AI experiences.
Build scalable AI platforms using Google Cloud AI and ML services.
Lead architecture reviews, solution design workshops, and technical governance.
Design secure AI architectures compliant with banking, risk, regulatory, and data privacy requirements.
Partner with Product Owners, Data Engineering, Risk, Fraud, and Compliance teams to deliver business outcomes.
Establish AI MLOps, LLMOps, observability, monitoring, and responsible AI frameworks.
Drive AI adoption through reusable architecture patterns, frameworks, and best practices.
Provide technical leadership and mentoring to architects and engineering teams.
Required Skills:
Generative AI & Agentic AI
LLMs including GPT, Gemini, Claude, Llama, and open-source models.
RAG architecture, vector databases, embeddings, semantic search.
Multi-agent and Agentic AI frameworks.
Prompt Engineering and Prompt Optimization.
Fine-tuning approaches such as LoRA and QLoRA.
AI Guardrails, Responsible AI, Explainability, and Hallucination Reduction.
LangChain, LangGraph, LlamaIndex, Semantic Kernel.
GCP Architecture
Vertex AI
Gemini Models
Document AI
BigQuery
Dataflow
Pub/Sub
Cloud Run
GKE
Cloud Functions
Cloud Storage
Dataproc
IAM and Security Architecture
Customer Data Platform (CDP)
Enterprise Customer Data Platform architecture.
Customer 360° data modeling.
Identity Resolution and Master Data Management.
Real-time customer insights and personalization.
Data governance, consent management, and privacy controls.
Integration with marketing, analytics, and AI ecosystems.
Data & AI Engineering
Python
SQL
Distributed Data Processing
Feature Engineering
Vector Databases (Pinecone, Vertex AI Vector Search, Weaviate, FAISS)
Knowledge Graphs
Data Lake and Data Mesh concepts
Banking Domain Expertise
Banking Operations
Fraud Management
Risk & Controls
Regulatory Compliance
Customer Servicing
Operational Excellence Programs
Financial Crimes/Fraud Analytics