AI Developers

Toronto, ON
On-site
Posted Oct 1, 2026
1 position

Opens LinkedIn

Employment type: Full-time
Experience level: Mid-level · 2+ years
Posting language: English
Working hours: 40 hours per week
Seniority: Mid-Senior level

Job summary

Build and productionize generative AI applications, agents, retrieval solutions, and intelligent workflows integrated with enterprise systems. Implement evaluation, security controls, observability, and cloud-based deployment practices while collaborating with technical teams and business stakeholders.

Job details

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. Role Overview AI Developer As an AI Developer, you will build and productionize 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 â— Build Generative AI, AI agent, agentic workflow, enterprise search & retrieval, and intelligent automation solutions for clients. â— Develop AI agents that reason over enterprise data, use tools and APIs, maintain state, and execute multi-step workflows. â— Build RAG and agentic retrieval solutions across documents, databases, APIs, search platforms, and enterprise systems. â— Develop workflows using tool/function calling, structured outputs, memory, context engineering, and human-in-the-loop controls. â— Integrate AI applications with enterprise platforms, SaaS products, APIs, databases, and systems of record. â— Build reusable AI services, APIs, components, SDKs, and accelerators. â— Implement hybrid search, semantic retrieval, reranking, query rewriting, and knowledge access patterns. â— Build multimodal AI solutions using text, documents, images, audio, and other enterprise content. â— Implement AI evaluation, regression testing, guardrails, and quality controls. â— Instrument AI applications for tracing, observability, latency, token usage, cost, and agent behavior. â— Implement controls for prompt injection, data leakage, unsafe tool execution, and excessive agent permissions. â— Deploy AI workloads using cloud platforms, containers, CI/CD, and modern DevOps practices. â— Partner with architects, engineers, and business stakeholders to translate AI use cases into production-ready solutions. Qualifications â— 3+ years of hands-on software engineering experience, with meaningful experience building and shipping AI-powered applications. â— Strong hands-on experience with Python, JavaScript/TypeScript, APIs, SQL, and Git. â— Proven experience building AI agents, agentic workflows, and RAG or agentic retrieval solutions. â— Hands-on experience with LLMs and foundation models, such as OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, Cohere, or open-source models. â— Experience with AI frameworks such as LangChain/LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or Model Context Protocol (MCP). â— Experience with embeddings, semantic and hybrid search, reranking, and model routing and orchestration. â— Familiarity with search and vector platforms such as PostgreSQL/pgvector, Elasticsearch/OpenSearch, Azure AI Search, Pinecone, Qdrant, Weaviate, Redis, or Neo4j. â— Hands-on experience with at least one major cloud platform (Azure, AWS, or Google Cloud) and its AI services. â— Experience with Docker, Kubernetes, CI/CD, and Infrastructure as Code. â— Familiarity with AI observability and evaluation tools such as MLflow, LangSmith, Langfuse, or Arize Phoenix. â— Strong understanding of AI security practices, including prompt injection, data leakage, and agent permission controls. â— Excellent communication and problem-solving skills, with the ability to take AI solutions from prototype to production.

What you’ll do

Requirements

Requires at least three years of software engineering experience, including hands-on delivery of AI-powered applications, and proficiency in Python, JavaScript or TypeScript, APIs, SQL, and Git. Candidates should have experience with AI agents, RAG, LLMs, AI frameworks, search and vector platforms, cloud services, DevOps tools, AI evaluation, and security practices.