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GenAI Architect

Capgemini

Job Description

Job Title: Generative AI (GenAI) Architect

Location: NYC, NY (Hybrid)

Experience: 10+ Years in Software Engineering, Data & AI with 3+ Years in Generative AI, LLMs, and Enterprise AI Solutions

Job Summary

We are seeking an experienced Generative AI Architect to lead the design, development, and deployment of enterprise-scale AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and AI-powered automation. The ideal candidate will drive AI strategy, define architecture standards, and collaborate with business stakeholders, engineering teams, and leadership to deliver scalable, secure, and responsible AI solutions.

Key Responsibilities

AI Architecture & Strategy

  • Define and implement enterprise GenAI architecture and roadmap.
  • Design scalable AI solutions using OpenAI, Azure OpenAI, Anthropic Claude, Gemini, and open-source LLMs.
  • Evaluate emerging AI technologies and recommend adoption strategies.
  • Establish AI governance, security, compliance, and responsible AI frameworks.

Solution Design & Development

  • Architect and implement:
  • RAG (Retrieval-Augmented Generation) solutions
  • AI Agents and Multi-Agent Systems
  • Conversational AI and Chatbots
  • Knowledge Management Solutions
  • Document Intelligence Platforms
  • Design vector database architectures using Pinecone, Weaviate, ChromaDB, Qdrant, or Azure AI Search.
  • Develop prompt engineering and prompt management strategies.
  • Define scalable LLM orchestration patterns using LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or LangGraph.

Cloud & Infrastructure

  • Architect cloud-native AI solutions on:
  • Microsoft Azure
  • AWS
  • Google Cloud Platform
  • Design MLOps/LLMOps pipelines for deployment, monitoring, and optimization.
  • Establish model observability, monitoring, and performance tracking frameworks.
  • Optimize AI workloads for scalability, reliability, and cost efficiency.

Security & Compliance

  • Develop secure AI architectures aligned with enterprise security standards.
  • Implement data privacy controls, PII protection, model governance, and audit mechanisms.
  • Ensure compliance with regulatory frameworks such as GDPR, HIPAA, SOC2, and Responsible AI principles.

Stakeholder Management

  • Partner with business leaders to identify GenAI opportunities.
  • Translate business requirements into scalable technical solutions.
  • Lead architecture reviews, design sessions, and technical governance meetings.
  • Mentor AI engineers, architects, and development teams.

Required Qualifications

Education

  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.

Experience

  • 10+ years of software engineering, solution architecture, or data engineering experience.
  • 3+ years in AI/ML architecture and GenAI solution design.
  • Hands-on experience building enterprise-grade GenAI solutions.

Technical Skills

Generative AI & LLMs

  • OpenAI GPT-4/4o
  • Azure OpenAI Service
  • Anthropic Claude
  • Google Gemini
  • Llama, Mistral, Falcon

AI Frameworks

  • LangChain
  • LlamaIndex
  • Semantic Kernel
  • LangGraph
  • CrewAI
  • AutoGen

RAG & Search Technologies

  • Azure AI Search
  • Elasticsearch
  • Pinecone
  • ChromaDB
  • Weaviate
  • Qdrant

Programming

  • Python
  • Java
  • C#
  • REST APIs
  • Microservices Architecture

Cloud Platforms

  • Microsoft Azure (Preferred)
  • AWS
  • GCP

DevOps & MLOps

  • Docker
  • Kubernetes
  • Terraform
  • GitHub Actions
  • Azure DevOps
  • MLflow

Preferred Qualifications

  • Microsoft Azure AI Engineer Associate (AI-102)
  • Azure Solutions Architect Expert (AZ-305)
  • AWS Machine Learning Specialty
  • Google Professional Machine Learning Engineer
  • TOGAF Certification
  • Experience with Agentic AI and Autonomous Workflow Systems

Desired Skills

  • Enterprise Architecture
  • AI Governance
  • Responsible AI
  • Design Thinking
  • Stakeholder Management
  • Team Leadership
  • Strategic Planning
  • Solution Consulting

Key Deliverables

  • Enterprise GenAI Architecture Blueprint
  • AI Governance Framework
  • RAG and Agentic AI Solutions
  • LLM Platform Strategy
  • AI Security & Compliance Standards
  • Production-Ready AI Applications

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