AI Solutions Architect – Generative AI & Enterprise AI Strategy

Job Role: AI Solutions Architect Generative AI & Enterprise AI Strategy

Location: Canada/USA - Remote

No of position: 1

Exp. Start date: 2nd week of Aug

Year of Experience: 15+ Years

Type: Fulltime

Position Summary

We are seeking an experienced AI Solutions Architect with 15+ years of IT experience to lead the design, architecture, and implementation of enterprise-scale Artificial Intelligence solutions. The ideal candidate will possess deep expertise in Generative AI (GenAI), Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), AI Governance, and Enterprise AI Architecture, with a proven ability to drive AI adoption across enterprise business functions.

This role requires close collaboration with executive leadership, business stakeholders, product teams, and engineering organizations to build scalable, secure, and responsible AI solutions that accelerate business transformation.

Key Responsibilities

Define and execute enterprise AI strategy, architecture, and technology roadmap aligned with business objectives.
Design and architect scalable Generative AI, Agentic AI, AI Copilots, Retrieval-Augmented Generation (RAG), GraphRAG, and Knowledge Management solutions.
Evaluate, benchmark, and recommend appropriate LLMs including OpenAI GPT, Claude, Gemini, Llama, Mistral, Cohere, and other emerging foundation models.
Design AI applications utilizing Multi-Agent Systems, autonomous AI workflows, and intelligent orchestration frameworks.
Build enterprise AI architectures leveraging Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, and cloud-native AI services.
Lead architecture for LLMOps, model deployment, prompt management, AI monitoring, model evaluation, governance, and lifecycle management.
Design enterprise semantic search, vector search, embedding pipelines, and retrieval architectures using modern vector databases.
Establish AI governance, Responsible AI, security, compliance, privacy, and risk management frameworks.
Collaborate with engineering teams to integrate AI capabilities into enterprise applications using APIs, microservices, and event-driven architectures.
Optimize AI solution performance, inference latency, token utilization, scalability, and operational costs.
Provide technical leadership throughout the AI solution lifecycle from discovery and proof of concept through production deployment.
Partner with executive stakeholders to identify AI use cases, define business value, and lead enterprise AI transformation initiatives.
Mentor architects and engineering teams on AI architecture best practices and emerging technologies.

Required Skills & Experience

15+ years of overall IT experience.
7+ years in Solution Architecture or Enterprise Architecture.
3+ years of hands-on experience designing and delivering Generative AI solutions in enterprise environments.

Strong Expertise In

Generative AI
Large Language Models (LLMs)
Agentic AI
Multi-Agent Systems
AI Copilots
Prompt Engineering
Prompt Chaining
AI Reasoning Workflows

Experience Working With

OpenAI GPT
Claude
Gemini
Llama
Mistral
Cohere

Deep Understanding Of

Retrieval-Augmented Generation (RAG)
GraphRAG
Vector Databases
Embeddings
Semantic Search
Knowledge Graphs

Hands-on Experience With

LangChain
LangGraph
LlamaIndex
Semantic Kernel
CrewAI
AutoGen
MCP (Model Context Protocol)

Experience Building AI Solutions On

Azure AI Foundry
Azure OpenAI Service
AWS Bedrock
Google Vertex AI

Strong Knowledge Of

Python
REST APIs
Microservices
Docker
Kubernetes

Experience With

AI Governance
Responsible AI
LLMOps
Model Monitoring
Model Evaluation
AI Security
AI Compliance
Excellent consulting, communication, and executive stakeholder management skills.

Preferred Skills

Experience within Insurance, Banking, or Financial Services domains.
Experience implementing Enterprise AI Copilots, intelligent document processing, enterprise search, and conversational AI solutions.
Knowledge of enterprise data platforms including Microsoft Fabric, Snowflake, Databricks, or similar modern data ecosystems.
Experience integrating AI with enterprise platforms such as Microsoft 365, Salesforce, ServiceNow, SAP, or Oracle.
Expertise in AI cost optimization, inference optimization, model benchmarking, and token optimization.
Experience leading enterprise AI transformation programs and AI Centers of Excellence (CoE).
Azure, AWS, Google Cloud, OpenAI, or other AI-related certifications.