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Tiger Analytics

Agentic AI Architect

Canada

Full-time

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Job details

Job type: Full-time
Posting language: English
Posted: Oct 6, 2026

Original job description

Tiger Analytics is looking for experienced Agentic AI Architect with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

Requirements

We are seeking a visionary yet hands-on AI Solutions Architect to define, design, and bootstrap our next-generation Agentic AI architecture. In this role, you will bridge the gap between high-level business strategy and deep technical execution. You will be responsible for designing scalable, multi-agent AI ecosystems on cloud hyperscalers (Azure, AWS, or GCP), while staying close enough to the codebase to build prototypes, establish design patterns, and conduct rigorous code reviews.

The ideal candidate understands that an AI agent is more than just a prompt; it is a complex system involving orchestration, memory fabrics, guardrails, and tool execution. You will partner directly with business executives and engineering teams to transform ambitious AI product visions into production-ready enterprise platforms.

1. Architecture & Solution Design (50%)

Define the Blueprint: Design robust, secure, and scalable architectures for autonomous and semi-autonomous AI agents, multi-agent orchestration systems, and cognitive workflows

Hyperscaler Implementation: Architect cloud-native AI pipelines leveraging specialized services across Azure (e.g., Azure OpenAI, AI Studio, Azure Functions), AWS (e.g., Bedrock, SageMaker, AWS Lambda), or GCP (e.g., Vertex AI, Agent Builder, Cloud Run)

System Orchestration & Memory: Establish architectural patterns for LLM orchestration (LangChain, LangGraph, AutoGen, CrewAI), semantic memory storage (vector databases like Pinecone, Milvus, or cloud-native vector extensions), and state management

Integration & Scalability: Ensure seamless integration of AI agents with legacy enterprise systems, APIs, RPA tools, and real-time data streams while optimizing for cost, latency, and throughput

2. Hands-on Engineering & Prototyping (30%)

Rapid Prototyping: Actively write code to build Proof of Concepts (PoCs) and minimum viable products (MVPs) to validate architectural theories and de-risk complex agent workflows

Code Quality & Governance: Establish production-grade coding standards for AI engineering. Conduct deep-dive code reviews to ensure reliability, security, and optimal prompt/agent behavior

Framework Evaluation: Hands-on evaluation of emerging AI frameworks, tooling, and foundational models (open-source vs. proprietary) to determine enterprise viability

3. Stakeholder Collaboration & Leadership (20%)

Translate Business to Tech: Partner with product managers and business stakeholders to translate vague business problems into structured agentic workflows and concrete technical requirements

Technical Advocacy: Act as the subject matter expert on Agentic AI, educating internal engineering teams and advising executives on capabilities, limitations, and ROI

AI Governance & Safety: Collaborate with Security, Compliance, and Risk teams to architect robust AI guardrails (e.g., NeMo Guardrails, Llama Guard), ensuring data privacy, hallucination mitigation, and ethical AI deployment

Required Qualifications

Technical Experience:

Cloud Expertise: 5+ years of experience architecting cloud solutions on at least one major hyperscaler (Azure, AWS, or GCP), with a deep understanding of cloud-native microservices and serverless paradigms

AI/LLM Engineering: 2+ years of direct experience working with Large Language Models (LLMs) and building complex AI applications (RAG pipelines, autonomous agents, tool-calling systems)

Agentic Frameworks: Hands-on proficiency with orchestration frameworks such as LangGraph, Semantic Kernel, AutoGen, CrewAI, or LangChain

Development Skills: Strong production-grade programming skills in Python (preferred), TypeScript, or Java, along with experience in CI/CD pipelines for AI (LLMOps)

Data & Search Infrastructure: Proven experience with vector databases and enterprise search engines (e.g., Azure AI Search, Amazon OpenSearch, Vertex AI Search)

Leadership & Soft Skills:

Bilingual Communicator: Ability to explain complex machine learning or architectural concepts simply to non-technical business leaders
Pragmatic Mindset: Balanced approach between pioneering bleeding-edge AI trends and delivering stable, cost-effective enterprise software
Education: Bachelor's or Master's degree in Computer Science, Data Science, or a related technical field (or equivalent practical experience)
Preferred Certifications
- Azure Solutions Architect Expert / Azure AI Engineer Associate
- AWS Certified Solutions Architect - Professional / AWS Certified Machine Learning - Specialty
- Google Cloud Professional Cloud Architect / Professional Machine Learning Engineer

Applicants based in US and Canada are invited to apply.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.

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More about this job

Responsibilities

Design scalable, secure agentic AI architectures and cloud-native systems, including multi-agent orchestration, memory, integrations, and governance. Build prototypes, establish engineering standards, review code, evaluate AI frameworks and models, and advise business and technical stakeholders.

Requirements

Requires at least five years of cloud architecture experience and at least two years building LLM applications, along with hands-on experience in agent frameworks, production programming, CI/CD, vector databases, and enterprise search. A bachelor's or master's degree in a relevant technical field or equivalent practical experience is required, alongside strong communication skills and a pragmatic approach to enterprise AI.

Skills

Cloud Architecture
Agentic AI
Large Language Models
AI Solutions Architecture
Multi-Agent Orchestration
Python
TypeScript
Java
LangGraph
LangChain
Semantic Kernel
AutoGen
CrewAI
Retrieval-Augmented Generation
Vector Databases
AI Governance

Benefits

Career Development Opportunities
High Individual Responsibility

Visa sponsorship

Not detected in the job text

Keywords

Agentic AI
Generative AI
Large Language Models
Azure
AWS
Google Cloud Platform
Azure OpenAI
Amazon Bedrock
Vertex AI
LangGraph
LangChain
Semantic Kernel
AutoGen
CrewAI
Python
TypeScript
Java
Retrieval-Augmented Generation
Vector Databases
Pinecone
Milvus
Enterprise Search
LLMOps
CI/CD
Microservices
Serverless Computing
AI Governance
NeMo Guardrails
Llama Guard
AI Safety

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