AI Engineer

Placed. REMOTE; Ontario; Remote; CAD 125,000 - 150,000; Full time

Job summary

A health services company is seeking an AI Engineer to design and deploy intelligent agents using leading multi-modal LLMs. You will work on building advanced RAG pipelines and leverage vector databases to create real-world automation. The ideal candidate has extensive experience with AI development, specifically within GCP, and is proficient in various AI APIs. This role is remote, offering competitive pay and the chance to contribute to cutting-edge AI technologies.

Qualifications

• 3+ years in ML, AI, or backend engineering with LLM experience.
• Strong experience with GCP and deploying AI workloads in production.
• Hands-on with various AI APIs and technologies.

Responsibilities

• Build LLM-Powered Agents: Architect and implement autonomous agents.
• Design RAG Systems: Develop retrieval-augmented generation pipelines.
• Collaborate with teams to build production-ready intelligent workflows.

Skills

Machine Learning

AI Development

Python

GCP

Prompt Engineering

Tools

OpenAI API

Gemini

Claude

LangChain

LlamaIndex

Job description

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Please note, Placed is helping FH Health in the hiring process)

Job Title : AI Engineer

Location : Remote / Toronto, ON

Type : Full-time

Reports to : VP of Engineering / CTO

Company : FH Health

About the Role

We’re building a new class of intelligent agents —AI that thinks, reasons, and acts. As an AI Engineer , you’ll design and deploy agents using leading multi-modal LLMs from OpenAI, Google Gemini, Meta (LLaMA), Anthropic Claude, and DeepSeek. You’ll build advanced RAG pipelines , leverage vector databases , and deploy on Google Cloud Platform (GCP) to bring real-world automation to life. Your work will power next-generation workflows across text, voice, image, and structured data.

Key Responsibilities

• Build LLM-Powered Agents : Architect and implement autonomous and semi-autonomous agents using LLM APIs (OpenAI, Gemini, Claude, DeepSeek, Meta).
• Design RAG Systems : Develop retrieval-augmented generation pipelines using vector databases (FAISS, Pinecone, Weaviate, etc.).
• Multi-Modal Reasoning : Work across modalities—text, image, audio—especially with Gemini and GPT-4o capabilities.
• Tool + API Integration : Connect agents to third-party tools like Twilio (voice / SMS), internal services, and external APIs to perform real actions.
• Agent Frameworks : Experiment with and extend popular frameworks (LangChain, LlamaIndex, CrewAI, AutoGen, etc.) to support complex workflows.
• Prompt + Context Engineering : Design optimized system instructions, memory handling, and tool-calling chains for high-performance agent behavior.
• Collaborate with Product & Infra Teams : Build and ship production-ready intelligent workflows tightly aligned with business needs.

Must-Have Qualifications

3+ years in ML, AI, or backend engineering with LLM experience

Strong experience with GCP and deploying AI workloads in production

Hands-on with OpenAI , Gemini , Claude , Meta (LLaMA) , or DeepSeek APIs

Solid grasp of RAG architecture and vector search technologies

Proficiency in Python and agent tooling (LangChain, LlamaIndex, etc.)

Familiarity with multi-modal LLM use cases (text, image, voice)

Deep understanding of prompt engineering, chaining, and context window management

Nice-to-Have Skills

Experience with Twilio APIs or other voice / SMS / chat tools

Background with frameworks like AutoGPT, CrewAI, AutoGen , etc.

Experience fine-tuning models or working with custom embeddings

Knowledge of cloud cost optimization, caching, and observability in LLM pipelines

What You’ll Accomplish

First 30 Days : Deliver a working multi-modal agent prototype with GCP deployment

First 90 Days : Productionize a RAG-based AI agent with integrated third-party tools

First 6 Months : Define and evolve our modular agent architecture for long-term scalability

Why Join Us

This is your chance to help define the frontier of intelligent, action-oriented AI . You won’t just be working with LLMs — you’ll be building systems that use them to do real work, in real time, for real users.

Seniority level

Seniority level

Mid-Senior level

Employment type

Employment type

Full-time

Job function

Job function

Engineering and Information Technology

Industries

Human Resources Services

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