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111 of 472 matching · page 2 of 3

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TypeAgent infrastructure126 Agent engineering153 Developer tooling2 Applied research11 Forward deployed179 Other1

ModeRemote92 Hybrid95 On-site72 Location TBD213

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111 of 472 matching · page 2 of 3

Artificial Intelligence Developer
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Forward deployedSeniorTop pickNew

Artificial Intelligence Developer

BrainRidge ConsultingToronto, Ontario, Canada

Posted 1d ago/Full-time/Expires Nov 10, 2026

Apply on company site

Why it matters

The role ships production agents for clients that reason over enterprise data, call tools and APIs, maintain state, and execute multi-step workflows. It includes agent security controls, evaluation and regression testing, tracing, observability, retrieval, and integrations across enterprise systems.

About the job

BrainRidge Consulting is a premium FinTech Technology Consulting firm with the energy of a startup and the experience of an enterprise. With a mission to empower financial institutions and organizations to thrive in the digital age, we provide cutting-edge technology solutions and strategic guidance to drive innovation and growth. As we continue to grow, we are seeking motivated and experienced AI Developers to join our team.

As an AI Developer, you will build and produce modern AI applications, agents, and intelligent workflows for our clients. This is a hands-on engineering role for someone who combines strong software engineering fundamentals with deep AI expertise and can take solutions from prototype to secure, scalable production. This role is ideal for someone who thrives at the intersection of software engineering, applied AI, and enterprise integration, with a strong understanding of how AI drives business outcomes.

Key Responsibilities

Build Generative AI, AI agent, agentic workflow, enterprise search & retrieval, and intelligent automation solutions for clients.
Develop AI agents that reason over enterprise data, use tools and APIs, maintain state, and execute multi-step workflows.
Build RAG and agentic retrieval solutions across documents, databases, APIs, search platforms, and enterprise systems.
Develop workflows using tool/function calling, structured outputs, memory, context engineering, and human-in-the-loop controls.
Integrate AI applications with enterprise platforms, SaaS products, APIs, databases, and systems of record.
Build reusable AI services, APIs, components, SDKs, and accelerators.
Implement hybrid search, semantic retrieval, reranking, query rewriting, and knowledge access patterns.
Build multimodal AI solutions using text, documents, images, audio, and other enterprise content.
Implement AI evaluation, regression testing, guardrails, and quality controls.
Instrument AI applications for tracing, observability, latency, token usage, cost, and agent behavior.
Implement controls for prompt injection, data leakage, unsafe tool execution, and excessive agent permissions.
Deploy AI workloads using cloud platforms, containers, CI/CD, and modern DevOps practices.
Partner with architects, engineers, and business stakeholders to translate AI use cases into production-ready solutions.

Qualifications

3+ years of hands-on software engineering experience, with meaningful experience building and shipping AI-powered applications.
Strong hands-on experience with Python, JavaScript/TypeScript, APIs, SQL, and Git.
Proven experience building AI agents, agentic workflows, and RAG or agentic retrieval solutions.
Hands-on experience with LLMs and foundation models, such as OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, Cohere, or open-source models.
Experience with AI frameworks such as LangChain/LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or Model Context Protocol (MCP).
Experience with embeddings, semantic and hybrid search, reranking, and model routing and orchestration.
Familiarity with search and vector platforms such as PostgreSQL/pgvector, Elasticsearch/OpenSearch, Azure AI Search, Pinecone, Qdrant, Weaviate, Redis, or Neo4j.
Hands-on experience with at least one major cloud platform (Azure, AWS, or Google Cloud) and its AI services.
Experience with Docker, Kubernetes, CI/CD, and Infrastructure as Code.
Familiarity with AI observability and evaluation tools such as MLflow, LangSmith, Langfuse, or Arize Phoenix.
Strong understanding of AI security practices, including prompt injection, data leakage, and agent permission controls.
Excellent communication and problem-solving skills, with the ability to take AI solutions from prototype to production.

Stack

python
javascript
typescript
sql
langchain
langgraph
llamaindex
mcp

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