GenAI Solution Architect - Copilot

Centraprise; San Jose (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Centraprise is seeking an experienced GenAI Solution Architect to design, implement, and deploy enterprise-scale Generative AI solutions across cloud and hybrid environments. You will lead architecture, technology selection, and collaboration with stakeholders to ensure scalable, secure AI systems.

The role requires hands-on coding in Python/SQL with REST APIs, expertise in LLMs, RAG, AI Agents, and vector databases, and experience across Azure OpenAI, AWS Bedrock, and Google Vertex AI.

Qualifications

• Proven experience designing end-to-end Generative AI solutions.
• Strong knowledge of LLMs, RAG, AI Agents, Prompt Engineering, and Vector Databases.
• Experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar platforms.
• Strong hands-on coding experience in Python, SQL, REST APIs.
• Expertise in cloud-native architecture and distributed systems.

Responsibilities

• Design and defend end-to-end GenAI architectures across data, orchestration, integration, and security layers.
• Translate business needs into scalable, secure GenAI solutions.
• Evaluate and select LLMs, AI frameworks, and orchestration platforms.
• Lead deployments across Azure, AWS, and Google Cloud.
• Collaborate with stakeholders and mentor engineering teams.
• Develop POCs, accelerators, and reusable assets.

Skills

GenAI solution design

LLMs & RAG

Prompt engineering

Vector databases

Python

SQL

REST APIs

Cloud-native architecture

MLOps concepts

CI/CD

Education

Bachelor's or Master's in Computer Science / Engineering / IT

Tools

Docker

Kubernetes

Azure OpenAI

AWS Bedrock

Google Vertex AI

Pinecone

Weaviate

Chroma

Azure AI Search

Job description

Role Overview

We are seeking an experienced GenAI Solution Architect with strong expertise in designing, developing, and deploying enterprise-scale Generative AI solutions. The ideal candidate will be responsible for defining end-to-end architecture, guiding technical implementation, and collaborating with stakeholders to deliver scalable, secure, and business-aligned AI solutions across cloud and hybrid environments.

Key Responsibilities

• Design and defend end-to-end Generative AI architectures encompassing:
  • Data Layer
  • Orchestration Layer
  • Integration Layer
  • Security & Governance Layer
• Define architecture blueprints, technical standards, and best practices for GenAI implementations.
• Translate business requirements into scalable and secure AI-driven solutions.
• Evaluate and recommend appropriate LLMs, AI frameworks, vector databases, and orchestration platforms.

GenAI Implementation

• Develop and implement GenAI solutions using modern frameworks such as LangChain, LlamaIndex, Semantic Kernel, and AI orchestration platforms.
• Design and implement Retrieval-Augmented Generation (RAG), AI Agents, Prompt Engineering, and Knowledge Management solutions.
• Integrate AI solutions with enterprise applications, APIs, and data platforms.
• Lead deployments across cloud platforms such as Azure, AWS, and GCP.
• Architect hybrid solutions spanning on-premises and cloud environments.
• Ensure high availability, scalability, security, and performance of AI systems.

Hands-on Engineering

• Contribute actively to coding, solution development, and technical problem-solving.
• Review code, establish development standards, and mentor engineering teams.
• Develop proof-of-concepts (POCs), accelerators, and reusable assets.

Stakeholder Management

• Work closely with business stakeholders, product teams, and engineering groups.
• Present architectural solutions and technical recommendations to leadership and clients.
• Support technical discussions, architecture reviews, and client workshops.

Required Skills & Qualifications

Technical Skills

• Proven experience designing and implementing end-to-end Generative AI solutions.
• Strong knowledge of LLMs, RAG, AI Agents, Prompt Engineering, and Vector Databases.
• Experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar AI platforms.
• Strong hands-on coding experience in:
  • Python
  • SQL
  • REST APIs
• Expertise in cloud-native architecture and distributed systems.

Preferred Qualifications

• Experience with MLOps, LLMOps, CI/CD pipelines, and containerization technologies (Docker, Kubernetes).
• Familiarity with Vector Databases such as Pinecone, Weaviate, Chroma, or Azure AI Search.
• Certification in Azure, AWS, GCP, or AI-related technologies.
• Experience working in customer-facing consulting or solutioning roles.

Experience

• 12+ years of overall IT experience.
• 5+ years in Solution Architecture.
• 3+ years of hands-on experience in Generative AI, LLM, and AI solution development.

Education

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field.