Forward Deployed Engineer (FDE) - GenAI / Agentic AI
- Toronto, ON, Canada
Full-time
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Job details
- Job type
- Full-time
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- Posting language
- English
- Posted
Original job 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
Engineering Foundation
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.
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
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
- 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
- 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
- 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
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.
More about this job
Responsibilities
Partner with clients to translate complex business challenges into production-grade generative AI and agentic AI solutions, owning the process from initial discovery through deployment. Build, integrate, deploy, and optimize LLM applications, RAG systems, and AI agents in enterprise cloud environments, collaborating with technical teams and client stakeholders.
Requirements
Requires 7+ years of software, application, ML, or AI engineering experience, strong Python skills, and a solid foundation in APIs, system design, databases, cloud-native applications, and production deployments. Candidates should have hands-on experience delivering GenAI or agentic AI solutions, including LLMs, RAG, vector databases, agents, orchestration, and at least one major cloud platform.
Skills
- Python
- Generative AI
- Agentic AI
- Large Language Models
- Retrieval-Augmented Generation
- Vector Databases
- AI Agents
- Multi-Agent Systems
- LangGraph
- LangChain
- LlamaIndex
- Cloud Computing
- System Design
- API Integration
- Production Deployment
- Client Communication
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
- Consulting
Keywords
- Forward Deployed Engineering
- Generative AI
- Agentic AI
- Python
- JavaScript
- TypeScript
- Large Language Models
- OpenAI
- Anthropic Claude
- Google Gemini
- Retrieval-Augmented Generation
- Vector Databases
- Embeddings
- Tool Calling
- Function Calling
- AI Agents
- Multi-Agent Workflows
- LangGraph
- LangChain
- LlamaIndex
- Google ADK
- Model Context Protocol
- Agent-to-Agent Protocol
- AWS
- Amazon Bedrock
- OpenSearch Serverless
- Azure OpenAI Service
- Azure AI Search
- Google Cloud Vertex AI