Tiger Analytics

Delivers Data and AI transformation

Forward Deployed Engineer - GenAI, Agentic AI

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeToronto, ON, CanadaNo salary listedIn-person

About the Job

Requirements

Seven or more years in software engineering, application development, machine-learning engineering, or AI engineering.
Strong Python programming skills.
A solid grasp of APIs, system design, databases, cloud-native applications, and production deployments.
Proven experience building and deploying end-to-end generative AI and/or agentic AI solutions.
Hands-on experience with large language models, including OpenAI, Anthropic Claude, Google Gemini, and open-source models.
Hands-on experience with retrieval-augmented generation (RAG), vector databases, embeddings, tool or function calling, and AI agents.
Experience with agent orchestration, multi-agent systems, or agentic workflows.
Familiarity with frameworks such as LangGraph, LangChain, LlamaIndex, Google ADK, or similar.
Working knowledge of Model Context Protocol (MCP), Agent-to-Agent (A2A), or similar agent integration protocols.
Hands-on experience with at least one major cloud provider: Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
For AWS: designing workflows with Amazon Bedrock (Claude, Llama), deploying scalable RAG using OpenSearch Serverless Vector Engine, and configuring Bedrock Guardrails for client virtual private clouds (VPCs).
For Azure: deploying enterprise large language models via Azure OpenAI Service with provisioned throughput unit (PTU) optimization, building hybrid RAG systems with Azure AI Search, and orchestrating flows using Azure AI Foundry and Semantic Kernel.
For GCP: scaling applications within Vertex AI, managing production pipelines, structuring enterprise RAG with Vertex AI Vector Search and BigQuery, and building agentic flows with Gemini APIs.
Exposure to large language model evaluation, observability, monitoring, guardrails, or LLMOps.
Strong customer-facing communication and problem-solving skills.
Ability to operate independently, navigate ambiguity, and move quickly from problem to solution to production.
Ability to work effectively with business and technical stakeholders.

Responsibilities

Partner directly with clients to understand complex business problems and translate them into generative AI and agentic AI solutions.
Design, build, deploy, and scale large language model applications, RAG systems, AI agents, and multi-agent workflows.
Integrate AI solutions with enterprise data, APIs, databases, and business applications.
Deploy, operate, and scale solutions in production cloud environments.
Troubleshoot and optimize production systems for performance, scalability, reliability, security, and cost.
Collaborate with architects, machine-learning engineers, developers, and client stakeholders to deliver solutions from initial discussions through live deployment.

Desired Qualifications

JavaScript or TypeScript experience.

Forward Deployed Engineer (FDE) - GenAI / Agentic AI

Tiger Analytics Inc. · Toronto, Canada · Full-time · Posted 2026-09-30

Workplace: on_site

Department: MLE

Description

Tiger Analytics is looking for experienced Forward Deployed Engineer 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.

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

Role Overview:

We're looking for a Forward Deployed Engineer who thrives at the intersection of client problem-solving and hands-on AI engineering. You'll partner directly with clients and engineering teams to turn complex business challenges into production-grade GenAI and Agentic AI solutions — owning the journey from first conversation to live deployment.

Requirements

Partner directly with clients to understand complex business problems and translate them into GenAI and Agentic AI solutions.
Design, build, deploy, and scale LLM applications, RAG systems, AI agents, and multi-agent workflows.
Integrate AI solutions with enterprise data, APIs, databases, and business applications.
Deploy, operate, and scale solutions in production cloud environments.
Troubleshoot and optimize production systems for performance, scalability, reliability, security, and cost.
Collaborate closely with architects, ML engineers, developers, and client stakeholders in fast-moving, ambiguous environments.

Qualifications:

Engineering Foundation

7+ years in software engineering, application development, ML engineering, or AI engineering.
Strong Python programming skills.
Solid grasp of APIs, system design, databases, cloud-native applications, and production deployments.
JavaScript/TypeScript experience is a plus.

GenAI / Agentic AI Expertise

Proven experience building and deploying end-to-end GenAI and/or Agentic AI solutions.
Hands-on experience with LLMs — OpenAI, Anthropic Claude, Google Gemini, and open-source models.
Hands-on experience with RAG, vector databases, embeddings, tool/function calling, and AI agents.
Experience with agent orchestration, multi-agent systems, or agentic workflows.
Familiarity with frameworks such as LangGraph, LangChain, LlamaIndex, Google ADK, or similar.
Working knowledge of MCP, A2A, or similar agent integration protocols.

Cloud & Production

Hands-on experience with at least one major hyperscaler — AWS, Azure, or GCP — including targeted expertise in:
AWS Stack: Designing workflows via Amazon Bedrock (Claude, Llama), deploying scalable RAG using OpenSearch Serverless (Vector Engine), and configuring Bedrock Guardrails for client VPCs.
Azure Stack: Deploying enterprise LLMs via Azure OpenAI Service (PTU optimization), building hybrid RAG systems with Azure AI Search, and orchestrating flows using Azure AI Foundry and Semantic Kernel.
GCP Stack: Scaling applications within Vertex AI, managing production pipelines, structuring enterprise RAG with Vertex AI Vector Search + BigQuery, and building agentic flows with Gemini APIs.
Exposure to LLM evaluation, observability, monitoring, guardrails, or LLMOps.

The FDE Mindset

Strong customer-facing communication and sharp problem-solving instincts.
Comfortable operating independently, navigating ambiguity, and moving fast from problem → solution → production.
Equally at home with business and technical stakeholders.

Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging 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.

Apply

Apply at Tiger Analytics Inc.

About the Company

Tiger Analytics

Delivers Data and AI transformation

Tiger Analytics helps large organizations plan and execute Data and AI–led transformations at scale. It works with clients to translate business needs into data and AI initiatives, using an approach that combines business value mapping, agile operating models, and a platforms-based architecture, backed by partnerships with major hyperscalers. The company designs and delivers end-to-end data and AI solutions—from strategy and data engineering to model development and deployment—so that AI can be applied to tough, real-world problems. Unlike firms that rely mainly on standard playbooks, Tiger Analytics builds custom approaches and accelerators to suit each client’s value chain and operating model. With a global team of 5000+ technologists and consultants, the goal is to enable measurable value and practical outcomes through data and AI programs that work at enterprise scale.

Company size: 1,001-5,000
Company stage: Not listed
Total funding: Not listed
Headquarters: San Jose, CA
Founded: 2010

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